title
Artificial Intelligence Full Course in 10 Hours [2024] | Artificial Intelligence Tutorial | Edureka

description
🔥 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 : https://www.edureka.co/masters-program/machine-learning-engineer-training (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") This Edureka video on "Artificial Intelligence Full Course" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples. Below are the topics covered in this Artificial Intelligence Full Course: 00:00:00 Introduction to Artificial Intelligence Full Course 00:01:16 Agenda of Artificial Intelligence Full Course 00:02:22 What is AI 00:15:38 Examples of AI 00:21:16 Deep learning IS machine learning 00:26:53 AI Explained 00:28:19 AI Vs ML Vs DL 00:29:09 Importance of AI 00:31:23 Types of AI 00:32:53 Applications of AI 00:37:33 Domains of AI 00:40:21 Job Profiles in AI 00:43:54 Object Detection 00:55:28 How to become an AI Engineer 01:07:55 Stages of AI 01:12:54 Domains of AI 01:15:42 AI With Python 01:38:50 Introduction to ML 01:49:42 Types of ML 01:59:13 ML Algorithm 02:14:17 Limitations of ML 02:16:15 Introduction to DL 02:20:09 Use Cases of DL 02:43:05 NLP 02:47:13 What is NLP 02:48:13 Applications of NLP 03:05:56 TensorFlow Explained 03:15:41 TensorFlow 03:22:17 Hands-On 03:30:48 Convolutional Neural Networks 03:36:00 Convolutional Layer 03:46:44 Use Cases 03:56:25 What are Artificial Neural Networks 04:08:43 Training a Neural Network 04:20:05 Applications of Neural Network 04:23:11 Recurrent Neural Network 04:35:10 Long Short-Term Memory Networks 04:44:47 Long Short-Term Memory Networks - Use Case 04:52:04 Keras 05:02:40 Use Case With Keras 05:17:10 A* Algorithm in AI 05:41:52 Cognitive AI 05:46:51 COgnitive AI - Use Cases 05:50:14 Q Learning Explained 06:04:49 Transitioning to Q Learning 06:16:37 Water Jug Problem in AI 06:38:21 ChatGpt Explained 06:47:59 Dangers of AI 06:53:48 What AI is Like Right Now? 07:00:28 Mid-term dangers 07:08:43 What Does the Future Hold 07:10:41 Knowledge Representation in AI 07:26:10 Hill Climbing Algorithm 07:55:20 TOp 10 APplications of AI 08:09:59 Top 10 AI technologies 08:19:03 Top 10 Benefits of AI 08:30:52 AI Roadmap 08:42:21 AI Interview Questions & Answers 🔴 Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 🔵 DevOps Online Training: http://bit.ly/3VkBRUT 🌕 AWS Online Training: http://bit.ly/3ADYwDY 🔵 React Online Training: http://bit.ly/3Vc4yDw 🌕 Tableau Online Training: http://bit.ly/3guTe6J 🔵 Power BI Online Training: http://bit.ly/3VntjMY 🌕 Selenium Online Training: http://bit.ly/3EVDtis 🔵 PMP Online Training: http://bit.ly/3XugO44 🌕 Salesforce Online Training: http://bit.ly/3OsAXDH 🔵 Cybersecurity Online Training: http://bit.ly/3tXgw8t 🌕 Java Online Training: http://bit.ly/3tRxghg 🔵 Big Data Online Training: http://bit.ly/3EvUqP5 🌕 RPA Online Training: http://bit.ly/3GFHKYB 🔵 Python Online Training: http://bit.ly/3Oubt8M 🌕 Azure Online Training: http://bit.ly/3i4P85F 🔵 GCP Online Training: http://bit.ly/3VkCzS3 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC 🌕 Cloud Architect Masters Program: http://bit.ly/3OvueZy 🔵 Data Scientist Masters Program: http://bit.ly/3tUAOiT 🌕 Big Data Architect Masters Program: http://bit.ly/3tTWT0V 🔵 Machine Learning Engineer Masters Program: http://bit.ly/3AEq4c4 🌕 Business Intelligence Masters Program: http://bit.ly/3UZPqJz 🔵 Python Developer Masters Program: http://bit.ly/3EV6kDv 🌕 RPA Developer Masters Program: http://bit.ly/3OteYfP 🔵 Web Development Masters Program: http://bit.ly/3U9R5va 🌕 Computer Science Bootcamp Program: http://bit.ly/3UZxPBy 🔵 Cyber Security Masters Program: http://bit.ly/3U25rNR 🌕 Full Stack Developer Masters Program: http://bit.ly/3tWCE2S 🔵 Automation Testing Engineer Masters Program: http://bit.ly/3AGXg2J 🌕 Python Developer Masters Program: https://bit.ly/3EV6kDv 🔵 Azure Cloud Engineer Masters Program: http://bit.ly/3AEBHzH 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬 🌕 Professional Certificate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT 🔵 Advanced Certificate Program in Data Science & AI with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh 📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 2023 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢 ⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g 📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates 📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain 📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka 📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/ 📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/ 📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN Got a question on the topic? Please share it in the comment section below and our experts will answer. Please write to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.

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{'title': 'Artificial Intelligence Full Course in 10 Hours [2024] | Artificial Intelligence Tutorial | Edureka', 'heatmap': [{'end': 1425.899, 'start': 1067.378, 'weight': 1}, {'end': 2858.382, 'start': 2490.855, 'weight': 0.898}], 'summary': "This 10-hour artificial intelligence tutorial covers practical applications, ai's impact on domains, job profiles, machine learning, deep learning, neural networks, lstms, rnn, wine data analysis, space science, q-learning, dangers of ai, knowledge representation, ai's impact and applications, ai technologies across industries, careers in data science and robotics, ml models, hyperparameters, overfitting prevention, and ai applications, providing quantifiable insights and examples.", 'chapters': [{'end': 688.944, 'segs': [{'end': 35.811, 'src': 'embed', 'start': 4.406, 'weight': 0, 'content': [{'end': 9.03, 'text': 'Artificial Intelligence is high in demand and is rapidly growing in popularity.', 'start': 4.406, 'duration': 4.624}, {'end': 19.56, 'text': 'As businesses and organizations are seeking to leverage technology to improve their operations, AI is becoming an essential tool for automating tasks,', 'start': 9.871, 'duration': 9.689}, {'end': 22.082, 'text': 'making predictions and improving decision-making.', 'start': 19.56, 'duration': 2.522}, {'end': 24.804, 'text': 'Hello everyone and welcome to this session.', 'start': 22.843, 'duration': 1.961}, {'end': 29.148, 'text': 'You are currently watching an Edureka Artificial Intelligence full course video.', 'start': 25.305, 'duration': 3.843}, {'end': 35.811, 'text': "Well, I'm certain by the end of this video you will have a thorough understanding about artificial intelligence,", 'start': 30.069, 'duration': 5.742}], 'summary': 'Ai is in high demand, rapidly growing, and essential for automating tasks and improving decision-making.', 'duration': 31.405, 'max_score': 4.406, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM4406.jpg'}, {'end': 199.749, 'src': 'embed', 'start': 178.579, 'weight': 2, 'content': [{'end': 187.683, 'text': 'The term artificial intelligence was first coined decades ago in the year 1956 by John McCarty at the Dartmouth conference.', 'start': 178.579, 'duration': 9.104}, {'end': 194.146, 'text': 'He defined artificial intelligence as the science and engineering of making intelligent machines.', 'start': 188.304, 'duration': 5.842}, {'end': 199.749, 'text': 'In a sense, AI is a technique of getting machines to work and behave like humans.', 'start': 194.807, 'duration': 4.942}], 'summary': 'Ai was coined in 1956 by john mccarty as the science of making intelligent machines.', 'duration': 21.17, 'max_score': 178.579, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM178579.jpg'}, {'end': 523.445, 'src': 'embed', 'start': 492.898, 'weight': 3, 'content': [{'end': 497.203, 'text': 'the applications of AI have covered all possible domains in the market.', 'start': 492.898, 'duration': 4.305}, {'end': 499.173, 'text': 'In the finance sector.', 'start': 497.831, 'duration': 1.342}, {'end': 504.7, 'text': "JPMorgan's Chase Contract Intelligence platform uses artificial intelligence,", 'start': 499.173, 'duration': 5.527}, {'end': 513.712, 'text': 'machine learning and image recognition software to analyze legal documents and extract important data points and clauses in a matter of seconds.', 'start': 504.7, 'duration': 9.012}, {'end': 523.445, 'text': 'Now, manually reviewing 12,000 agreements takes over 36,000 hours, but AI was able to do this in a matter of seconds.', 'start': 514.332, 'duration': 9.113}], 'summary': "Jpmorgan's ai platform analyzes 12,000 agreements in seconds, saving 36,000 hours", 'duration': 30.547, 'max_score': 492.898, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM492898.jpg'}, {'end': 609.049, 'src': 'embed', 'start': 539.697, 'weight': 4, 'content': [{'end': 550.28, 'text': 'IBM Watson AI technology was able to cross-reference 20 million oncology records and correctly diagnose a rare leukemia condition in a patient.', 'start': 539.697, 'duration': 10.583}, {'end': 553.001, 'text': 'Coming to the next application.', 'start': 550.8, 'duration': 2.201}, {'end': 557.542, 'text': "Google's AI eye doctor is another initiative taken by Google,", 'start': 553.001, 'duration': 4.541}, {'end': 569.386, 'text': 'where they are working with an Indian eye care chain to develop an AI system which can examine retina scans and identify a condition called diabetic retinopathy which causes blindness.', 'start': 557.542, 'duration': 11.844}, {'end': 572.647, 'text': 'Coming to social media platforms like Facebook.', 'start': 569.926, 'duration': 2.721}, {'end': 576.795, 'text': 'Artificial intelligence is used for face verification,', 'start': 573.314, 'duration': 3.481}, {'end': 582.817, 'text': 'wherein machine learning and deep learning concepts are used to detect facial features and tag your friends.', 'start': 576.795, 'duration': 6.022}, {'end': 591, 'text': "Another such example is Twitter's AI, which is being used to identify hate speech and terroristic languages in tweets.", 'start': 583.477, 'duration': 7.523}, {'end': 597.943, 'text': 'It makes use of machine learning, deep learning, and natural language processing to filter out offensive content.', 'start': 591.6, 'duration': 6.343}, {'end': 609.049, 'text': 'The company discovered and banned 300,000 terrorist-linked accounts, 95% of which were found by non-human, artificially intelligent machines.', 'start': 598.444, 'duration': 10.605}], 'summary': 'Ibm watson diagnosed rare leukemia; google ai identifies diabetic retinopathy; facebook and twitter ai filter out offensive content and terrorist-linked accounts.', 'duration': 69.352, 'max_score': 539.697, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM539697.jpg'}], 'start': 4.406, 'title': "Ai's practical applications and basics", 'summary': 'Discusses the increasing demand for ai in businesses, its role in automation, predictions, and decision-making. it also covers ai basics, types, and applications in finance, healthcare, social media, and virtual assistants, with examples and differentiation between narrow, general, and super intelligence.', 'chapters': [{'end': 59.777, 'start': 4.406, 'title': 'Ai in demand & practical applications', 'summary': "Discusses the growing demand for artificial intelligence in businesses and organizations, highlighting its role in automating tasks, making predictions, and improving decision-making. it also promotes edureka's ai certification course and encourages viewers to subscribe to their youtube channel.", 'duration': 55.371, 'highlights': ['AI is rapidly growing in popularity and high in demand, as businesses and organizations seek to leverage technology to improve their operations.', 'AI is becoming an essential tool for automating tasks, making predictions, and improving decision-making.', "The video promotes Edureka's AI certification course and encourages viewers to subscribe to their YouTube channel."]}, {'end': 688.944, 'start': 60.919, 'title': 'Ai basics and applications', 'summary': 'Covers the basics of artificial intelligence including its definition, types, and applications in various domains such as finance, healthcare, social media, and virtual assistants, with examples and quantifiable data, highlighting the difference between artificial narrow intelligence, artificial general intelligence, and artificial super intelligence.', 'duration': 628.025, 'highlights': ['The term artificial intelligence was first coined in 1956 by John McCarty at the Dartmouth conference, defining AI as the science and engineering of making intelligent machines, which has now found applications in various fields including healthcare, finance, and social media.', "AI is used in the finance sector, where JPMorgan's Chase Contract Intelligence platform uses AI, machine learning, and image recognition software to analyze legal documents, reducing the review time from 36,000 hours to a matter of seconds.", 'IBM Watson AI technology was able to cross-reference 20 million oncology records and correctly diagnose a rare leukemia condition in a patient, showcasing the potential of AI in healthcare with quantifiable data.', "Google's AI eye doctor can examine retina scans and identify diabetic retinopathy, a condition causing blindness, demonstrating the practical use of AI in healthcare with quantifiable impact.", "Twitter's AI is used to identify hate speech and terroristic languages in tweets, resulting in the discovery and banning of 300,000 terrorist-linked accounts, 95% of which were found by non-human, artificially intelligent machines, showing the effectiveness of AI in content moderation on social media."]}], 'duration': 684.538, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM4406.jpg', 'highlights': ['AI is rapidly growing in popularity and high in demand, as businesses and organizations seek to leverage technology to improve their operations.', 'AI is becoming an essential tool for automating tasks, making predictions, and improving decision-making.', 'The term artificial intelligence was first coined in 1956 by John McCarty at the Dartmouth conference, defining AI as the science and engineering of making intelligent machines, which has now found applications in various fields including healthcare, finance, and social media.', "AI is used in the finance sector, where JPMorgan's Chase Contract Intelligence platform uses AI, machine learning, and image recognition software to analyze legal documents, reducing the review time from 36,000 hours to a matter of seconds.", 'IBM Watson AI technology was able to cross-reference 20 million oncology records and correctly diagnose a rare leukemia condition in a patient, showcasing the potential of AI in healthcare with quantifiable data.', "Google's AI eye doctor can examine retina scans and identify diabetic retinopathy, a condition causing blindness, demonstrating the practical use of AI in healthcare with quantifiable impact.", "Twitter's AI is used to identify hate speech and terroristic languages in tweets, resulting in the discovery and banning of 300,000 terrorist-linked accounts, 95% of which were found by non-human, artificially intelligent machines, showing the effectiveness of AI in content moderation on social media."]}, {'end': 2583.776, 'segs': [{'end': 739.252, 'src': 'embed', 'start': 710.658, 'weight': 0, 'content': [{'end': 717.16, 'text': "Elon Musk talks a ton about how AI is implemented in Tesla's self-driving cars and autopilot features.", 'start': 710.658, 'duration': 6.502}, {'end': 725.328, 'text': 'He quoted that Tesla will have fully self-driving cars ready by the end of the year and a robo-taxi version,', 'start': 717.78, 'duration': 7.548}, {'end': 728.831, 'text': 'one that can ferry passengers without anyone behind the wheel.', 'start': 725.328, 'duration': 3.503}, {'end': 732.91, 'text': 'So, I can go on and on about the various AI applications.', 'start': 729.429, 'duration': 3.481}, {'end': 739.252, 'text': 'Since the emergence of AI in 1950s, we have seen an exponential growth in its potential.', 'start': 733.51, 'duration': 5.742}], 'summary': "Elon musk discusses ai implementation in tesla's self-driving cars, aiming for fully self-driving cars and robo-taxi by year end.", 'duration': 28.594, 'max_score': 710.658, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM710658.jpg'}, {'end': 779.618, 'src': 'embed', 'start': 753.846, 'weight': 1, 'content': [{'end': 761.85, 'text': 'As AI is branching out into every aspect of our lives, is it possible that one day AI might take over our lives?', 'start': 753.846, 'duration': 8.004}, {'end': 764.991, 'text': 'And if it is possible, how long will this take??', 'start': 762.39, 'duration': 2.601}, {'end': 767.312, 'text': 'Well, it may be sooner than you think.', 'start': 765.531, 'duration': 1.781}, {'end': 772.154, 'text': 'It is estimated that AI will take over the world within the next 30 years.', 'start': 767.712, 'duration': 4.442}, {'end': 779.618, 'text': 'By then, I hope we develop some sort of teleportation machine that helps us escape our very own creation.', 'start': 772.695, 'duration': 6.923}], 'summary': 'Ai may take over the world within 30 years, prompting hope for escape with teleportation technology.', 'duration': 25.772, 'max_score': 753.846, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM753846.jpg'}, {'end': 875.589, 'src': 'embed', 'start': 827.604, 'weight': 2, 'content': [{'end': 832.188, 'text': 'But why? Well, the reason is earlier we had very small amount of data.', 'start': 827.604, 'duration': 4.584}, {'end': 836.152, 'text': 'The data we had was not enough to predict the accurate result.', 'start': 832.589, 'duration': 3.563}, {'end': 839.816, 'text': "But now there's a tremendous increase in the amount of data.", 'start': 836.653, 'duration': 3.163}, {'end': 849.842, 'text': 'Statistics suggest that by 2020, the accumulated volume of data will increase from 4.4 zettabytes to roughly around 44 zettabytes,', 'start': 840.436, 'duration': 9.406}, {'end': 854.745, 'text': 'or 44 trillion GBs of data, along with such enormous amount of data.', 'start': 849.842, 'duration': 4.903}, {'end': 861.91, 'text': 'Now we have more advanced algorithm and high-end computing power and storage that can deal with such large amount of data.', 'start': 855.346, 'duration': 6.564}, {'end': 874.569, 'text': 'As a result, it is expected that 70% of Enterprise will implement AI over the next 12 months, which is up from 40% in 2016 and 51% in 2017,', 'start': 862.66, 'duration': 11.909}, {'end': 875.589, 'text': 'just for your understanding.', 'start': 874.569, 'duration': 1.02}], 'summary': 'Increase in data volume leads to 70% enterprise ai implementation in 12 months.', 'duration': 47.985, 'max_score': 827.604, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM827604.jpg'}, {'end': 1425.899, 'src': 'heatmap', 'start': 1067.378, 'weight': 1, 'content': [{'end': 1071.859, 'text': 'So our main goal is to minimize the error and make them as small as possible.', 'start': 1067.378, 'duration': 4.481}, {'end': 1079.841, 'text': 'decreasing the error or the difference between the actual value and estimated value increases the performance of the model further on the more data points.', 'start': 1071.859, 'duration': 7.982}, {'end': 1087.263, 'text': 'We collect the better our model will become we can also improve our model by adding more variables and creating different prediction lines for them.', 'start': 1079.881, 'duration': 7.382}, {'end': 1088.903, 'text': 'Once the line is created.', 'start': 1087.803, 'duration': 1.1}, {'end': 1093.764, 'text': 'So from the next time, if we feed a new data, for example height of a person, to the model,', 'start': 1089.163, 'duration': 4.601}, {'end': 1098.745, 'text': 'it would easily predict the data for you and it will tell you what is predicted weight could be.', 'start': 1093.764, 'duration': 4.981}, {'end': 1100.966, 'text': 'I hope you got a clear understanding of machine learning.', 'start': 1098.745, 'duration': 2.221}, {'end': 1102.146, 'text': 'So moving on ahead.', 'start': 1101.326, 'duration': 0.82}, {'end': 1103.867, 'text': "Let's learn about deep learning.", 'start': 1102.346, 'duration': 1.521}, {'end': 1106.007, 'text': 'Now, what is deep learning?', 'start': 1104.427, 'duration': 1.58}, {'end': 1112.588, 'text': 'you can consider deep learning model as a rocket engine and its fuel is its huge amount of data that we feed to these algorithms.', 'start': 1106.007, 'duration': 6.581}, {'end': 1115.289, 'text': 'The concept of deep learning is not new.', 'start': 1113.449, 'duration': 1.84}, {'end': 1119.523, 'text': 'but recently its hype has increased and deep learning is getting more attention.', 'start': 1116.019, 'duration': 3.504}, {'end': 1126.37, 'text': 'This field is a particular kind of machine learning that is inspired by the functionality of our brain cells, called neuron,', 'start': 1120.324, 'duration': 6.046}, {'end': 1128.953, 'text': 'which led to the concept of artificial neural network.', 'start': 1126.37, 'duration': 2.583}, {'end': 1135.749, 'text': 'It simply takes the data connection between all the artificial neurons and adjust them according to the data pattern.', 'start': 1129.844, 'duration': 5.905}, {'end': 1138.311, 'text': 'more neurons are added at the size of the data is large.', 'start': 1135.749, 'duration': 2.562}, {'end': 1142.955, 'text': 'It automatically features learning at multiple levels of abstraction,', 'start': 1138.772, 'duration': 4.183}, {'end': 1148.861, 'text': 'thereby allowing a system to learn complex function mapping without depending on any specific algorithm.', 'start': 1142.955, 'duration': 5.906}, {'end': 1154.746, 'text': 'You know what no one actually knows what happens inside a neural network and why it works so well.', 'start': 1149.301, 'duration': 5.445}, {'end': 1157.188, 'text': 'So currently you can call it as a black box.', 'start': 1155.106, 'duration': 2.082}, {'end': 1161.568, 'text': 'Let us discuss some of the example of deep learning and understand it in a better way.', 'start': 1157.927, 'duration': 3.641}, {'end': 1166.89, 'text': 'Let me start with a simple example and explain you how things happen at a conceptual level.', 'start': 1162.068, 'duration': 4.822}, {'end': 1171.692, 'text': 'Let us try and understand how you recognize a square from other shapes.', 'start': 1167.53, 'duration': 4.162}, {'end': 1177.553, 'text': 'The first thing you do is you check whether there are four lines associated with the figure or not.', 'start': 1172.252, 'duration': 5.301}, {'end': 1178.334, 'text': 'simple concept, right?', 'start': 1177.553, 'duration': 0.781}, {'end': 1182.035, 'text': 'If yes, we further check if they are connected and closed.', 'start': 1178.954, 'duration': 3.081}, {'end': 1189.929, 'text': 'Again, if yes, we finally check whether it is perpendicular and all its sides are equal correct if everything fulfills.', 'start': 1182.68, 'duration': 7.249}, {'end': 1191.211, 'text': 'Yes, it is a square.', 'start': 1190.17, 'duration': 1.041}, {'end': 1196.277, 'text': 'Well, it is nothing but a nested hierarchy of Concepts what we did here.', 'start': 1191.872, 'duration': 4.405}, {'end': 1201.384, 'text': 'We took a complex task of identifying a square in this case and broke it into simpler task.', 'start': 1196.578, 'duration': 4.806}, {'end': 1205.762, 'text': 'Now this deep learning also does the same thing but at a larger scale.', 'start': 1202.1, 'duration': 3.662}, {'end': 1214.065, 'text': "Let's take an example of machine which recognizes the animal the task of the machine is to recognize whether the given image is of a cat or of a dog.", 'start': 1206.262, 'duration': 7.803}, {'end': 1219.447, 'text': 'What if we were asked to resolve the same issue using the concept of machine learning what we would do.', 'start': 1214.785, 'duration': 4.662}, {'end': 1227.723, 'text': 'First, we would define the features, such as check whether the animal has whiskers or not, or check if the animal has pointed ears or not,', 'start': 1220.097, 'duration': 7.626}, {'end': 1230.185, 'text': 'or whether its tail is straight or curved.', 'start': 1227.723, 'duration': 2.462}, {'end': 1237.531, 'text': 'in short, we will define the facial features and let the system identify which features are more important in classifying a particular animal.', 'start': 1230.185, 'duration': 7.346}, {'end': 1240.334, 'text': 'Now when it comes to deep learning.', 'start': 1238.112, 'duration': 2.222}, {'end': 1242.575, 'text': 'it takes this to one step ahead.', 'start': 1240.334, 'duration': 2.241}, {'end': 1246.879, 'text': 'deep learning automatically finds out the feature which are most important for classification.', 'start': 1242.575, 'duration': 4.304}, {'end': 1252.219, 'text': 'compared to machine learning where we had to manually give out that features by now.', 'start': 1247.515, 'duration': 4.704}, {'end': 1257.403, 'text': 'I guess you have understood that AI is a bigger picture and machine learning and deep learning are its subpart.', 'start': 1252.499, 'duration': 4.904}, {'end': 1262.427, 'text': "So let's move on and focus our discussion on machine learning and deep learning.", 'start': 1257.944, 'duration': 4.483}, {'end': 1270.054, 'text': 'the easiest way to understand the difference between the machine learning and deep learning is to know that deep learning is machine learning more specifically.', 'start': 1262.427, 'duration': 7.627}, {'end': 1272.416, 'text': 'It is the next evolution of machine learning.', 'start': 1270.354, 'duration': 2.062}, {'end': 1276.339, 'text': "Let's take few important parameter and compare machine learning with deep learning.", 'start': 1272.956, 'duration': 3.383}, {'end': 1283.799, 'text': 'So, starting with data dependencies, the most important difference between deep learning and machine learning is its performance,', 'start': 1276.934, 'duration': 6.865}, {'end': 1287.001, 'text': 'as the volume of the data gets increased from the below graph.', 'start': 1283.799, 'duration': 3.202}, {'end': 1292.545, 'text': "You can see that when the size of the data is small, deep learning algorithm doesn't perform that well.", 'start': 1287.202, 'duration': 5.343}, {'end': 1293.766, 'text': 'but why?', 'start': 1292.545, 'duration': 1.221}, {'end': 1300.431, 'text': 'well, this is because deep learning algorithm needs a large amount of data to understand it perfectly.', 'start': 1293.766, 'duration': 6.665}, {'end': 1305.355, 'text': 'on the other hand, the machine learning algorithm can easily work with smaller data set fine.', 'start': 1300.431, 'duration': 4.924}, {'end': 1308.384, 'text': 'Next comes the hardware dependencies.', 'start': 1306.142, 'duration': 2.242}, {'end': 1315.67, 'text': 'deep learning algorithms are heavily dependent on high-end machines, while the machine learning algorithm can work on low-end machines as well.', 'start': 1308.384, 'duration': 7.286}, {'end': 1323.075, 'text': 'This is because the requirement of deep learning algorithm include GPUs, which is an integral part of its working.', 'start': 1316.37, 'duration': 6.705}, {'end': 1329, 'text': 'the deep learning algorithm required GPUs, as they do a large amount of matrix multiplication operations,', 'start': 1323.075, 'duration': 5.925}, {'end': 1335.585, 'text': 'and these operations can only be efficiently optimized using a GPU as it is built for this purpose only.', 'start': 1329, 'duration': 6.585}, {'end': 1338.918, 'text': 'Our third parameter will be feature engineering.', 'start': 1336.417, 'duration': 2.501}, {'end': 1348.264, 'text': 'Well, feature engineering is a process of putting the domain knowledge to reduce the complexity of the data and make patterns more visible to learning algorithms.', 'start': 1339.559, 'duration': 8.705}, {'end': 1353.647, 'text': 'This process is difficult and expensive in terms of time and expertise.', 'start': 1348.964, 'duration': 4.683}, {'end': 1361.111, 'text': 'in case of machine learning, most of the features are needed to be identified by an expert and then hand coded as per the domain and the data type.', 'start': 1353.647, 'duration': 7.464}, {'end': 1369.139, 'text': 'For example, the features can be a pixel value, shapes, texture, position, orientation or anything fine.', 'start': 1361.693, 'duration': 7.446}, {'end': 1376.285, 'text': 'the performance of most of the machine learning algorithm depends on how accurately the features are identified and extracted,', 'start': 1369.139, 'duration': 7.146}, {'end': 1380.749, 'text': 'whereas in case of deep learning algorithms it try to learn high-level features from the data.', 'start': 1376.285, 'duration': 4.464}, {'end': 1386.254, 'text': 'This is a very distinctive part of deep learning which makes it way ahead of traditional machine learning.', 'start': 1381.29, 'duration': 4.964}, {'end': 1391.841, 'text': 'Deep learning reduces the task of developing new feature extractor for every problem.', 'start': 1387.02, 'duration': 4.821}, {'end': 1398.482, 'text': 'like in the case of CNN algorithm, it first try to learn the low-level features of the image, such as edges and lines,', 'start': 1391.841, 'duration': 6.641}, {'end': 1404.103, 'text': 'and then it proceeds to the parts of faces of people and then finally to the high-level representation of the face.', 'start': 1398.482, 'duration': 5.621}, {'end': 1406.384, 'text': 'I hope the things are getting clear to you.', 'start': 1404.763, 'duration': 1.621}, {'end': 1409.584, 'text': "So let's move on ahead and see the next parameter.", 'start': 1407.084, 'duration': 2.5}, {'end': 1412.305, 'text': 'So our next parameter is problem-solving approach.', 'start': 1410.045, 'duration': 2.26}, {'end': 1416.412, 'text': 'When we are solving a problem using traditional machine learning algorithm.', 'start': 1413.129, 'duration': 3.283}, {'end': 1421.956, 'text': 'It is generally recommended that we first break down the problem into different sub parts,', 'start': 1416.652, 'duration': 5.304}, {'end': 1425.899, 'text': 'solve them individually and then finally combine them to get the desired result.', 'start': 1421.956, 'duration': 3.943}], 'summary': 'Minimize error, increase data for better model, deep learning is data-dependent, hardware-dependent, and features learning at multiple levels.', 'duration': 358.521, 'max_score': 1067.378, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM1067378.jpg'}, {'end': 1323.075, 'src': 'embed', 'start': 1293.766, 'weight': 4, 'content': [{'end': 1300.431, 'text': 'well, this is because deep learning algorithm needs a large amount of data to understand it perfectly.', 'start': 1293.766, 'duration': 6.665}, {'end': 1305.355, 'text': 'on the other hand, the machine learning algorithm can easily work with smaller data set fine.', 'start': 1300.431, 'duration': 4.924}, {'end': 1308.384, 'text': 'Next comes the hardware dependencies.', 'start': 1306.142, 'duration': 2.242}, {'end': 1315.67, 'text': 'deep learning algorithms are heavily dependent on high-end machines, while the machine learning algorithm can work on low-end machines as well.', 'start': 1308.384, 'duration': 7.286}, {'end': 1323.075, 'text': 'This is because the requirement of deep learning algorithm include GPUs, which is an integral part of its working.', 'start': 1316.37, 'duration': 6.705}], 'summary': 'Deep learning needs large data and high-end machines, machine learning works with smaller data and low-end machines.', 'duration': 29.309, 'max_score': 1293.766, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM1293766.jpg'}, {'end': 1642.776, 'src': 'embed', 'start': 1612.946, 'weight': 5, 'content': [{'end': 1616.848, 'text': 'ai is a technique that enables machines to mimic human behavior.', 'start': 1612.946, 'duration': 3.902}, {'end': 1624.512, 'text': 'artificial intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence,', 'start': 1616.848, 'duration': 7.664}, {'end': 1631.376, 'text': 'such as visual perception, speech recognition, decision making and translation between languages.', 'start': 1624.512, 'duration': 6.864}, {'end': 1636.839, 'text': 'now, if you ask me, ai is the simulation of human intelligence, then my machines programmed by us.', 'start': 1631.376, 'duration': 5.463}, {'end': 1642.776, 'text': 'The machines need to learn how to reason and do some self-correction as needed along the way,', 'start': 1637.492, 'duration': 5.284}], 'summary': 'Ai enables machines to mimic human behavior and perform tasks requiring human intelligence.', 'duration': 29.83, 'max_score': 1612.946, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM1612946.jpg'}, {'end': 2398.311, 'src': 'embed', 'start': 2372.458, 'weight': 6, 'content': [{'end': 2381.943, 'text': "Now these were the different domains of AI and it just tells us how wide AI is and it's just not confined to just one sort of area of development.", 'start': 2372.458, 'duration': 9.485}, {'end': 2384.584, 'text': 'Now, according to the job site,', 'start': 2382.623, 'duration': 1.961}, {'end': 2391.667, 'text': 'indeed the demand for AI skills has more than doubled over the last past three years and the number of job posting is up by 119%.', 'start': 2384.584, 'duration': 7.083}, {'end': 2398.311, 'text': 'but this artificial intelligence tutorial will be incomplete without the different job profiles.', 'start': 2391.667, 'duration': 6.644}], 'summary': 'Ai spans various domains, job demand has doubled in 3 years, job postings up by 119%.', 'duration': 25.853, 'max_score': 2372.458, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM2372458.jpg'}, {'end': 2474.681, 'src': 'embed', 'start': 2446.428, 'weight': 7, 'content': [{'end': 2453.211, 'text': 'They create programs that will enable machines to take actions without being specifically directed to perform those tasks,', 'start': 2446.428, 'duration': 6.783}, {'end': 2458.333, 'text': 'and they can earn a whooping hundred and ten thousand dollars per annum.', 'start': 2453.211, 'duration': 5.122}, {'end': 2460.174, 'text': "That's a huge amount of money.", 'start': 2458.794, 'duration': 1.38}, {'end': 2467.938, 'text': 'Now the next job profile is the data scientist and it has been awarded as the sexiest job of this 21st century.', 'start': 2460.935, 'duration': 7.003}, {'end': 2474.681, 'text': 'So data scientists are those who crack complex data problems with their strong expertise in certain specific disciplines.', 'start': 2468.378, 'duration': 6.303}], 'summary': 'Professionals can earn $110,000 annually by creating programs for autonomous machines. data scientists tackle complex data problems and have been named the sexiest job of the 21st century.', 'duration': 28.253, 'max_score': 2446.428, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM2446428.jpg'}, {'end': 2590.167, 'src': 'embed', 'start': 2561.809, 'weight': 9, 'content': [{'end': 2567.311, 'text': "It's a career that's in high demand and commands an annual median salary of $92,000.", 'start': 2561.809, 'duration': 5.502}, {'end': 2572.632, 'text': 'Now, the big data engineers and architects have among the best paying jobs in artificial intelligence.', 'start': 2567.311, 'duration': 5.321}, {'end': 2576.253, 'text': 'In fact, they command an annual median salary of $150,000.', 'start': 2573.133, 'duration': 3.12}, {'end': 2583.776, 'text': 'The big data solution architect is responsible for managing the full lifecycle of a Hadoop solution.', 'start': 2576.254, 'duration': 7.522}, {'end': 2590.167, 'text': 'This includes creating requirement, analysis, the platform selection, designing of the technical architecture,', 'start': 2584.363, 'duration': 5.804}], 'summary': 'Big data engineers and architects command high salaries, with median pay at $150,000, playing a crucial role in managing hadoop solutions.', 'duration': 28.358, 'max_score': 2561.809, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM2561809.jpg'}], 'start': 690.692, 'title': 'Ai and its impact on various domains', 'summary': 'Covers ai applications in self-driving cars, its future impact, rise of ai and machine learning with a surge in data volume, machine learning & deep learning basics and differences, and understanding artificial intelligence along with its domains and job profiles, including the surge in job demand and high-paying job profiles in the field.', 'chapters': [{'end': 827.203, 'start': 690.692, 'title': 'Ai applications and future impact', 'summary': "Discusses the applications of ai in self-driving cars and its potential impact, mentioning elon musk's plans for fully self-driving cars and the estimation that ai will take over the world within the next 30 years.", 'duration': 136.511, 'highlights': ["Elon Musk plans for fully self-driving cars and a robo-taxi version, estimated to be ready by the end of the year, implementing AI in Tesla's self-driving cars and autopilot features.", 'The estimation that AI will take over the world within the next 30 years, raising the question of its potential impact on our lives.', 'AI covers domains such as machine learning, deep learning, neural networks, natural language processing, knowledge base, and expert systems, showcasing its exponential growth and potential impact across various fields.', 'The explanation of how artificial intelligence, machine learning, and deep learning are related, with deep learning being a subset of machine learning, and both being subsets of AI.']}, {'end': 1032.152, 'start': 827.604, 'title': 'Rise of ai and machine learning', 'summary': "Discusses the surge in data volume, emphasizing a 10-fold increase from 4.4 zettabytes to 44 zettabytes by 2020, empowering a 70% enterprise adoption of ai in the next 12 months, and the evolution of ai from replicating human behavior to learning from experience. machine learning's inception, influenced by issues in statistics, neuroscience, and ai, led to its shift towards data-driven decision-making and continuous improvement.", 'duration': 204.548, 'highlights': ['The accumulated volume of data is projected to increase from 4.4 zettabytes to roughly around 44 zettabytes by 2020, indicating a tenfold surge, fostering a significant impact on AI implementation.', '70% of Enterprise is expected to implement AI over the next 12 months, showcasing a substantial rise from 40% in 2016 and 51% in 2017, reflecting the growing adoption of AI in the business landscape.', 'AI enables machines to replicate human behavior and learn from experiences, allowing them to adjust their responses based on new inputs, indicating a shift from merely mimicking human actions to adapting and evolving like humans.', 'Machine learning emerged due to issues in statistics, computer science, artificial intelligence, and neuroscience, driving its focus towards data-driven decision-making and continual enhancement, signifying a significant paradigm shift in its development.', "Machine learning is a subset of AI that enables computers to make data-driven decisions and improve over time when exposed to new data, exemplified by creating systems to predict a person's weight based on their height, highlighting its ability to learn and evolve."]}, {'end': 1584.291, 'start': 1032.612, 'title': 'Machine learning & deep learning', 'summary': 'Discusses the basics and differences between machine learning and deep learning, emphasizing the need for large data sets, hardware dependencies, feature engineering, problem-solving approaches, and execution time, with examples and explanations.', 'duration': 551.679, 'highlights': ['Deep learning requires a large amount of data to understand it perfectly, while machine learning can work with smaller data sets. Deep learning algorithm needs a large amount of data to understand it perfectly, while machine learning algorithm can easily work with smaller data set.', 'Deep learning algorithms are heavily dependent on high-end machines, requiring GPUs for efficient optimization, while machine learning algorithms can work on low-end machines as well. Deep learning algorithms are heavily dependent on high-end machines, requiring GPUs for efficient optimization, while machine learning algorithms can work on low-end machines as well.', 'Feature engineering in machine learning requires domain knowledge and expert identification of features, while deep learning algorithms try to learn high-level features from the data, reducing the task of developing new feature extractors for every problem. Feature engineering in machine learning requires domain knowledge and expert identification of features, while deep learning algorithms try to learn high-level features from the data, reducing the task of developing new feature extractors for every problem.', 'Deep learning algorithm solves the problem from end to end, while machine learning algorithm breaks down the problem into different sub-parts and solves them individually. Deep learning algorithm solves the problem from end to end, while machine learning algorithm breaks down the problem into different sub-parts and solves them individually.', 'Deep learning algorithms take longer to train but less time to run during testing, while machine learning algorithms take less time to train but more time to run during testing as the data size increases. Deep learning algorithms take longer to train but less time to run during testing, while machine learning algorithms take less time to train but more time to run during testing as the data size increases.']}, {'end': 2240.562, 'start': 1584.992, 'title': 'Understanding artificial intelligence', 'summary': 'Delves into the concept of artificial intelligence, its subfields like machine learning and deep learning, its historical background, importance, types, and diverse applications, highlighting its transformative impact, including examples of ai in sports, rescue missions, wildlife poaching prevention, smart agriculture, and healthcare.', 'duration': 655.57, 'highlights': ['Artificial intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision making, and translation between languages. Describes the fundamental concept of artificial intelligence and its capabilities in performing tasks that typically require human intelligence.', 'The techniques from deep learning, image classification, object recognition, can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists. Demonstrates the impact of AI in healthcare, specifically in diagnosing diseases with high accuracy comparable to expert professionals.', 'Artificial intelligence automates repetitive learning and discovery through data, performs high volume computerized tasks reliably, and adds intelligence to existing products. Emphasizes the automation and improvement of products through AI, leading to increased efficiency and reliability in performing tasks.', 'Narrow AI is an artificial intelligence system designed and trained for one particular task, while wide AI has cognitive abilities to find solutions for unfamiliar tasks and replicate many capabilities of human intelligence. Explains the distinction between narrow AI and wide AI, highlighting their respective functionalities and cognitive abilities.', 'The use of AI and technology ensures faster help in rescue missions, predicts approaching threat levels for wildlife conservation, offers smart agricultural solutions, and enhances healthcare through machine learning enabled tools. Showcases diverse applications of AI in rescue missions, wildlife conservation, agriculture, and healthcare, emphasizing its transformative impact in various domains.']}, {'end': 2583.776, 'start': 2241.163, 'title': 'Ai domains and job profiles', 'summary': 'Discusses different domains of ai, including neural networks, robotics, expert systems, fuzzy logic, and natural language processing, along with a surge in ai job demand and various high-paying job profiles in the field, with machine learning engineers earning up to $110,000 per annum.', 'duration': 342.613, 'highlights': ['The demand for AI skills has more than doubled over the last past three years and the number of job postings is up by 119%. The surge in demand for AI skills is evidenced by a doubling in demand over three years and a 119% increase in job postings, indicating a significant growth trend in the AI job market.', 'Machine learning engineers can earn a whooping hundred and ten thousand dollars per annum, making it one of the high-paying job profiles in the AI field. Machine learning engineers can command a high annual salary of $110,000, indicating the lucrative nature of job opportunities in the AI field.', 'Data scientists have been awarded as the sexiest job of this 21st century and can earn an average salary of ninety two hundred thousand dollars per annum. Data scientists have been recognized as highly sought-after professionals, with an average annual salary of $92,000, indicating the desirability and earning potential of this job profile in the AI field.', 'Artificial intelligence engineers may earn an annual salary of around $105,000, showcasing the competitive compensation for professionals in the AI engineering field. Artificial intelligence engineers can command a substantial annual salary of approximately $105,000, reflecting the competitive compensation offered in the AI engineering sector.', 'Big data engineers and architects command an annual median salary of $150,000, making them among the best paying jobs in artificial intelligence. Big data engineers and architects are among the highest-paid professionals in the AI field, with an annual median salary of $150,000, highlighting the lucrative nature of these job roles.']}], 'duration': 1893.084, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM690692.jpg', 'highlights': ["Elon Musk plans for fully self-driving cars and a robo-taxi version, estimated to be ready by the end of the year, implementing AI in Tesla's self-driving cars and autopilot features.", 'The estimation that AI will take over the world within the next 30 years, raising the question of its potential impact on our lives.', 'The accumulated volume of data is projected to increase from 4.4 zettabytes to roughly around 44 zettabytes by 2020, indicating a tenfold surge, fostering a significant impact on AI implementation.', '70% of Enterprise is expected to implement AI over the next 12 months, showcasing a substantial rise from 40% in 2016 and 51% in 2017, reflecting the growing adoption of AI in the business landscape.', 'Deep learning requires a large amount of data to understand it perfectly, while machine learning can work with smaller data sets.', 'Artificial intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision making, and translation between languages.', 'The demand for AI skills has more than doubled over the last past three years and the number of job postings is up by 119%.', 'Machine learning engineers can earn a whooping hundred and ten thousand dollars per annum, making it one of the high-paying job profiles in the AI field.', 'Data scientists have been awarded as the sexiest job of this 21st century and can earn an average salary of ninety two hundred thousand dollars per annum.', 'Big data engineers and architects command an annual median salary of $150,000, making them among the best paying jobs in artificial intelligence.']}, {'end': 5264.584, 'segs': [{'end': 2652.009, 'src': 'embed', 'start': 2605.859, 'weight': 3, 'content': [{'end': 2609.182, 'text': 'And finally, if we have a look at the companies which are hiring,', 'start': 2605.859, 'duration': 3.323}, {'end': 2617.305, 'text': 'Companies that hire top AI talent range from startups like Argo AI to tech giants like IBM and according to Glassdoor.', 'start': 2609.781, 'duration': 7.524}, {'end': 2622.827, 'text': 'These are the leading employers who hired top AI talent over the past years.', 'start': 2617.545, 'duration': 5.282}, {'end': 2628.51, 'text': 'So as you can see we have Dropbox Adobe IBM LinkedIn Walmart.', 'start': 2623.667, 'duration': 4.843}, {'end': 2631.131, 'text': 'We have uber we have Red Hat and cheese.', 'start': 2628.59, 'duration': 2.541}, {'end': 2637.514, 'text': "Now, let's go ahead and start our demo and see how we can perform object detection using tensorflow.", 'start': 2632.131, 'duration': 5.383}, {'end': 2646.524, 'text': 'Now to begin with you want to make sure that you have tensorflow installed with all of its dependencies like the tensor board python matplotlib.', 'start': 2638.157, 'duration': 8.367}, {'end': 2652.009, 'text': "We have the cocoa API and the protobuf now, I'll explain you guys what all steps are needed.", 'start': 2646.744, 'duration': 5.265}], 'summary': 'Leading companies hiring top ai talent include startups like argo ai and tech giants like ibm. some of the top employers are dropbox, adobe, linkedin, and walmart.', 'duration': 46.15, 'max_score': 2605.859, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM2605859.jpg'}, {'end': 3387.187, 'src': 'embed', 'start': 3356.782, 'weight': 0, 'content': [{'end': 3368.052, 'text': 'AI market worth in 2020 was around 30 billion US dollars, and it is forecasted to rise at a whopping 35.6% compounded annual growth rate,', 'start': 3356.782, 'duration': 11.27}, {'end': 3376.619, 'text': "which is unprecedented for any industry, and it's going to rise to 300 billion US dollars by the year 2026..", 'start': 3368.052, 'duration': 8.567}, {'end': 3387.187, 'text': 'AI engineering has found its way in all sorts of industries, and applications of it can be seen in industries such as IT, transportation, finance,', 'start': 3376.619, 'duration': 10.568}], 'summary': 'Ai market projected to reach $300 billion by 2026 with a 35.6% annual growth rate.', 'duration': 30.405, 'max_score': 3356.782, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM3356782.jpg'}, {'end': 3441.793, 'src': 'embed', 'start': 3414.287, 'weight': 1, 'content': [{'end': 3418.37, 'text': 'This signifies that if we are to embrace AI fully,', 'start': 3414.287, 'duration': 4.083}, {'end': 3427.478, 'text': 'then it is of utmost importance that we understand the basics of AI and how the whole world is being transformed by it.', 'start': 3418.37, 'duration': 9.108}, {'end': 3434.125, 'text': "let's now move on to the next part of the section, which is job opportunities.", 'start': 3428.118, 'duration': 6.007}, {'end': 3441.793, 'text': 'in india, there are over 19 200 ai engineer jobs, and in united states that number is thirty thousand four hundred.', 'start': 3434.125, 'duration': 7.668}], 'summary': 'Understanding ai is crucial; india has 19,200 ai engineer jobs, us has 30,400.', 'duration': 27.506, 'max_score': 3414.287, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM3414287.jpg'}, {'end': 3860.258, 'src': 'embed', 'start': 3832.71, 'weight': 2, 'content': [{'end': 3838.732, 'text': 'along with that, they should also know cloud services like aws, gcp, azure okay.', 'start': 3832.71, 'duration': 6.022}, {'end': 3843.298, 'text': "so, with skills out of the way, let's see how to become an ai engineer, okay.", 'start': 3838.732, 'duration': 4.566}, {'end': 3845.1, 'text': "so here's the roadmap.", 'start': 3843.298, 'duration': 1.802}, {'end': 3853.071, 'text': 'first, you should have a formal education in computer science, mathematics, information technology, finance or economics, like we discussed earlier.', 'start': 3845.1, 'duration': 7.971}, {'end': 3860.258, 'text': "then it's time to hone your technical skills, such as programming skills, software development, life cycle, modularity,", 'start': 3853.692, 'duration': 6.566}], 'summary': 'To become an ai engineer, one should have formal education in computer science, mathematics, information technology, finance or economics and hone technical skills such as programming and software development.', 'duration': 27.548, 'max_score': 3832.71, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM3832710.jpg'}, {'end': 4766.593, 'src': 'embed', 'start': 4739.383, 'weight': 5, 'content': [{'end': 4744.085, 'text': 'Right so guys these are a couple of reasons as to why python is chosen for artificial intelligence.', 'start': 4739.383, 'duration': 4.702}, {'end': 4752.728, 'text': "It's actually considered the most popular and the most used language for data science AI machine learning and deep learning to prove that to you.", 'start': 4744.545, 'duration': 8.183}, {'end': 4760.251, 'text': 'Here is a stat from Stack Overflow Stack Overflow recently stated that python is the fastest growing programming language.', 'start': 4752.748, 'duration': 7.503}, {'end': 4766.593, 'text': 'If you look at the graph, you can see that it has taken over JavaScript and Java and C hash, C++ and PHP right,', 'start': 4760.271, 'duration': 6.322}], 'summary': "Python is chosen for ai due to its popularity and growth, as evidenced by stack overflow's stat.", 'duration': 27.21, 'max_score': 4739.383, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM4739383.jpg'}], 'start': 2584.363, 'title': 'Ai in various aspects', 'summary': "Covers ai job profiles, leading hiring companies, tensorflow object detection steps, ai engineering demand, opportunities, salaries, history and stages of ai, and python's key reasons and popular libraries. it discusses over 19,200 ai engineer jobs in india, more than 30,400 in the united states, and the potential salaries ranging from 15 to 30 lakhs in india and over $200,000 in the united states. it also outlines the key responsibilities and required skills for ai engineers and provides a roadmap for becoming an ai engineer.", 'chapters': [{'end': 2631.131, 'start': 2584.363, 'title': 'Ai job profiles and hiring companies', 'summary': 'Discusses the job profiles in ai, including requirement analysis, platform selection, technical architecture, application design, development, testing, and deployment. it also highlights leading companies hiring top ai talent, such as argo ai, ibm, dropbox, adobe, linkedin, walmart, uber, red hat, and chase.', 'duration': 46.768, 'highlights': ['The leading companies hiring top AI talent include Argo AI, IBM, Dropbox, Adobe, LinkedIn, Walmart, Uber, Red Hat, and Chase.', 'Job profiles in AI involve requirement analysis, platform selection, technical architecture, application design, development, testing, and deployment.']}, {'end': 3326.32, 'start': 2632.131, 'title': 'Tensorflow object detection', 'summary': 'Covers the steps for performing object detection using tensorflow, including installation of tensorflow and protobuf, downloading the tensorflow model, compiling protobuf, installing coco api, selecting a model based on system requirements, and running the object detection tutorial using ssd mobile net version 1 coco 2017 model to detect objects in images.', 'duration': 694.189, 'highlights': ['The chapter covers the steps for performing object detection using tensorflow, including installation of tensorflow and protobuf, downloading the tensorflow model, compiling protobuf, installing Coco API, selecting a model based on system requirements, and running the object detection tutorial using SSD mobile net version 1 Coco 2017 model to detect objects in images. This highlights the comprehensive overview of the steps involved in performing object detection using tensorflow, providing a broad understanding of the process and the necessary components involved.', 'The tensorflow object detection model uses protobuf to configure model and the training parameters, and the protobuf libraries must be compiled before the framework can be used. This emphasizes the importance of protobuf in configuring the detection model and training parameters, highlighting the prerequisite of compiling protobuf libraries before using the framework.', 'The Cocoa API installation is essential for loading, parsing, and visualizing annotations in Cocoa, which comprises a large image dataset designed for object detection, segmentation, and caption generation. This highlights the significance of Cocoa API in facilitating the loading, parsing, and visualization of annotations in Cocoa, emphasizing the extensive capabilities of the dataset for various detection and segmentation tasks.', 'The chapter details the selection of an appropriate model based on system specifications, emphasizing the importance of considering system resources such as GPU and RAM for optimal model performance. This provides insights into the critical consideration of selecting a model based on system resources, emphasizing the impact of GPU and RAM on the performance of the chosen model.', 'The tutorial demonstrates the process of running object detection using the SSD mobile net version 1 Coco 2017 model, illustrating the steps for importing libraries, selecting the model, downloading the model, loading the graph, and performing inference on test images. This highlights the practical demonstration of performing object detection using a specific model, providing a step-by-step guide on importing libraries, selecting, downloading, and using the model for inference on test images.']}, {'end': 3917.24, 'start': 3332.711, 'title': 'Ai engineering: demand, opportunities, and salaries', 'summary': 'Discusses the increasing demand for ai, with the market worth forecasted to rise to $300 billion by 2026, the availability of over 19,200 ai engineer jobs in india and 30,400 in the united states, and the potential salaries ranging from 15 to 30 lakhs in india and over $200,000 in the united states. it also outlines the key responsibilities and required skills for ai engineers, and provides a roadmap for becoming an ai engineer.', 'duration': 584.529, 'highlights': ['The AI market worth in 2020 was around 30 billion US dollars and is forecasted to rise to 300 billion US dollars by the year 2026, with a compounded annual growth rate of 35.6%, indicating unprecedented growth for the industry. The significant growth of the AI market, with a forecasted rise to $300 billion by 2026, demonstrates the increasing demand for AI technology and its applications across various industries.', 'There are over 19,200 AI engineer jobs in India and 30,400 in the United States, with major companies such as Amazon, Microsoft, Google, Tesla, and more actively hiring AI engineers. The availability of over 19,200 AI engineer jobs in India and 30,400 in the United States, along with the active recruitment by major companies, signifies the abundant job opportunities in the field of AI engineering.', 'AI engineers with over five years of experience are making 15 to 30 lakhs in India and well over $200,000 in the United States, indicating the potential for high salaries in the field. The potential salaries ranging from 15 to 30 lakhs in India and over $200,000 in the United States for experienced AI engineers illustrate the lucrative nature of careers in AI engineering.', 'The roles and responsibilities of AI engineers include developing machine learning applications, researching and implementing appropriate machine learning algorithms, running machine learning tests and experiments, and keeping abreast of the latest developments in the field of AI. The diverse roles and responsibilities of AI engineers, such as developing machine learning applications and staying updated with the latest AI developments, highlight the multifaceted nature of the profession.', 'The required skills for AI engineers include programming skills, a good grasp of math including linear algebra, calculus, statistics, and probability, knowledge of machine learning algorithms, natural language processing, deep learning and neural networks, and familiarity with big data technologies and cloud services. The extensive range of skills required for AI engineers, encompassing programming, mathematics, machine learning, deep learning, big data technologies, and cloud services, emphasizes the diverse expertise needed in the field.', 'The roadmap for becoming an AI engineer involves formal education in relevant fields, honing technical skills, learning essential technologies and concepts, specializing in artificial intelligence and machine learning, and building hands-on demos and projects to stand out during job applications. The comprehensive roadmap for becoming an AI engineer, including formal education, skill development, specialization, and practical projects, provides a clear guide for individuals aspiring to pursue a career in AI engineering.']}, {'end': 4520.664, 'start': 3917.24, 'title': 'History and stages of ai', 'summary': 'Covers the history of ai, from ancient mythology to recent achievements, and explains the three stages of ai, including weak ai, strong ai, and superintelligence, as well as the different types and domains of ai.', 'duration': 603.424, 'highlights': ['Artificial Superintelligence Artificial Superintelligence, a hypothetical stage where computers surpass human capabilities, is mentioned as a potential future threat, as it could lead to machines thinking and reasoning better than humans.', 'Artificial General Intelligence Artificial General Intelligence, also known as Strong AI, is described as the evolution of AI where machines possess the ability to think and make decisions like humans, with the potential to create machines as smart as humans.', "History of AI The chapter provides a historical overview of AI, from the classical ages and the proposal of the Turing test to significant milestones such as the first AI laboratory, introduction of the first robot to the assembly line, and IBM's Deep Blue beating the world chess champion.", 'Types of AI The different types of AI, including Weak AI, Strong AI, and Superintelligence, are explained, detailing their characteristics and potential impact on human existence, with a focus on the distinction between the types and stages of AI.', 'Domains of AI The various domains of AI, such as machine learning, deep learning, natural language processing, robotics, expert systems, and fuzzy logic, are outlined along with real-world applications and examples, providing insights into their roles in solving problems and advancing technology.']}, {'end': 5264.584, 'start': 4521.245, 'title': 'Python for ai: key reasons and popular libraries', 'summary': 'Discusses why python is chosen for artificial intelligence, highlighting reasons such as less coding, support for pre-built libraries, ease of learning, platform independence, and massive community support, backed by a stat from stack overflow. additionally, it provides detailed insights into popular python libraries for ai such as tensorflow, scikit-learn, numpy, theano, keras, and nltk, detailing their features and applications.', 'duration': 743.339, 'highlights': ["Python's popularity in AI, machine learning, and deep learning Python is considered popular for AI, machine learning, and deep learning due to reasons like less coding, support for pre-built libraries, ease of learning, platform independence, and massive community support. Stack Overflow stat confirms Python as the fastest growing programming language.", 'TensorFlow Responsive construct, flexibility, support for CPU and GPU training, parallel neural network training, and large community support', 'scikit-learn Cross-validation, wide range of algorithms, essential for feature extraction in images and texts', 'NumPy Array interface, support for multi-dimensional arrays, simplifies complex mathematical implementations, and ease of use', 'Theano Tight integration with NumPy, transparent use of GPU, error detection and diagnosis, and industry standard for deep learning', 'Keras Smooth running on CPU and GPU, support for various neural network models, and simplicity in debugging', 'NLTK Natural language text analysis, text mining, stemming, lemmatization, and tokenization']}], 'duration': 2680.221, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM2584363.jpg', 'highlights': ['The AI market worth in 2020 was around 30 billion US dollars and is forecasted to rise to 300 billion US dollars by the year 2026, with a compounded annual growth rate of 35.6%, indicating unprecedented growth for the industry.', 'There are over 19,200 AI engineer jobs in India and 30,400 in the United States, with major companies such as Amazon, Microsoft, Google, Tesla, and more actively hiring AI engineers.', 'The required skills for AI engineers include programming skills, a good grasp of math including linear algebra, calculus, statistics, and probability, knowledge of machine learning algorithms, natural language processing, deep learning and neural networks, and familiarity with big data technologies and cloud services.', 'The leading companies hiring top AI talent include Argo AI, IBM, Dropbox, Adobe, LinkedIn, Walmart, Uber, Red Hat, and Chase.', 'The chapter covers the steps for performing object detection using tensorflow, including installation of tensorflow and protobuf, downloading the tensorflow model, compiling protobuf, installing Coco API, selecting a model based on system requirements, and running the object detection tutorial using SSD mobile net version 1 Coco 2017 model to detect objects in images.', "Python's popularity in AI, machine learning, and deep learning Python is considered popular for AI, machine learning, and deep learning due to reasons like less coding, support for pre-built libraries, ease of learning, platform independence, and massive community support. Stack Overflow stat confirms Python as the fastest growing programming language."]}, {'end': 7076.248, 'segs': [{'end': 5877.905, 'src': 'embed', 'start': 5853.742, 'weight': 0, 'content': [{'end': 5862.706, 'text': 'machine learning is the subset of artificial intelligence that focuses on getting machines to make decisions by feeding them data.', 'start': 5853.742, 'duration': 8.964}, {'end': 5870.21, 'text': 'deep learning, on the other hand, is a subset of machine learning that uses the concept of neural networks to solve complex problems.', 'start': 5862.706, 'duration': 7.504}, {'end': 5877.905, 'text': 'So, to sum it up to you, artificial intelligence, machine learning and deep learning are heavily interconnected fields.', 'start': 5871.002, 'duration': 6.903}], 'summary': 'Ai, machine learning, and deep learning are interconnected fields.', 'duration': 24.163, 'max_score': 5853.742, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM5853742.jpg'}, {'end': 6054.866, 'src': 'embed', 'start': 6026.705, 'weight': 1, 'content': [{'end': 6031.507, 'text': "So don't worry if you haven't got the exact idea of what machine learning is,", 'start': 6026.705, 'duration': 4.802}, {'end': 6039.61, 'text': 'and the machine learning process involves building a predictive model that can be used to find a solution for a particular problem.', 'start': 6031.507, 'duration': 8.103}, {'end': 6043.432, 'text': 'a well-defined machine learning process will have around seven steps.', 'start': 6039.61, 'duration': 3.822}, {'end': 6049.481, 'text': 'It always begins with defining the objective followed by data gathering or data collection.', 'start': 6044.136, 'duration': 5.345}, {'end': 6054.866, 'text': 'Then we have something known as preparing data, which is also called data pre-processing.', 'start': 6050.082, 'duration': 4.784}], 'summary': 'Machine learning process includes 7 steps, starting with defining objective and data collection.', 'duration': 28.161, 'max_score': 6026.705, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM6026705.jpg'}, {'end': 6317.804, 'src': 'embed', 'start': 6293.18, 'weight': 2, 'content': [{'end': 6300.087, 'text': 'So guys, this stage is all about getting deep into your data and finding all the hidden data mysteries.', 'start': 6293.18, 'duration': 6.907}, {'end': 6306.033, 'text': 'EDA or exploratory data analysis is like the brainstorming stage of machine learning.', 'start': 6300.928, 'duration': 5.105}, {'end': 6310.957, 'text': 'Data exploration involves understanding the patterns and the trends in your data.', 'start': 6306.733, 'duration': 4.224}, {'end': 6317.804, 'text': 'So at this stage, all the useful insights are drawn and any correlations between the variables are understood.', 'start': 6311.538, 'duration': 6.266}], 'summary': 'Eda is crucial for uncovering hidden data patterns and correlations in machine learning.', 'duration': 24.624, 'max_score': 6293.18, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM6293180.jpg'}, {'end': 6884.976, 'src': 'embed', 'start': 6853.231, 'weight': 3, 'content': [{'end': 6855.312, 'text': 'This is exactly how reinforcement learning works.', 'start': 6853.231, 'duration': 2.081}, {'end': 6862.267, 'text': 'It involves an agent which is basically you stuck on the island that is put in an unknown environment,', 'start': 6855.945, 'duration': 6.322}, {'end': 6868.75, 'text': 'which is the island where the agent must learn by observing and performing actions that result in rewards.', 'start': 6862.267, 'duration': 6.483}, {'end': 6875.732, 'text': 'Reinforcement learning is mainly used in advanced machine learning areas such as self-driving cars, AlphaGo and so on.', 'start': 6869.45, 'duration': 6.282}, {'end': 6878.574, 'text': 'So guys, that sums up the types of machine learning.', 'start': 6876.293, 'duration': 2.281}, {'end': 6884.976, 'text': "Before we go any further, I'd like to discuss the difference between supervised, unsupervised and reinforcement learning.", 'start': 6879.174, 'duration': 5.802}], 'summary': 'Reinforcement learning involves an agent learning in unknown environment, used in advanced ml like self-driving cars and alphago.', 'duration': 31.745, 'max_score': 6853.231, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM6853231.jpg'}], 'start': 5265.154, 'title': 'Ai and machine learning', 'summary': 'Covers essential python libraries for ai, the evolution of ai, the machine learning process, types and applications of machine learning, and an overview of reinforcement learning, providing a comprehensive understanding of the subject.', 'chapters': [{'end': 5305.791, 'start': 5265.154, 'title': 'Essential python libraries for ai', 'summary': 'Discusses essential python-based libraries for machine learning, deep learning, and artificial intelligence, providing an overview and offering additional resources for further learning.', 'duration': 40.637, 'highlights': ['The chapter discusses essential Python-based libraries for implementing machine learning, deep learning, and artificial intelligence, providing an overview and offering additional resources for further learning.', 'The speaker assures the audience that they will cover and explain all the essential terms related to Python-based libraries by the end of the session.', 'The speaker encourages the audience to explore additional resources by providing links in the description box for further learning about the discussed libraries.']}, {'end': 6127.051, 'start': 5306.331, 'title': 'Evolution of ai and its impact', 'summary': 'Discusses the historical development of ai, from its origins in the 19th century to its recent exponential growth, driven by factors like computational power, data, algorithms, and investment. it also explores the distinctions between ai, machine learning, and deep learning, and their interconnectedness, culminating in a detailed explanation of the machine learning process.', 'duration': 820.72, 'highlights': ["Artificial intelligence (AI) has a rich history dating back to the 19th century, with a major breakthrough in 1950 by Alan Turing's proposal of the Turing test, and has witnessed exponential growth in recent years due to factors such as increased computational power, availability of big data, advancements in algorithms like neural networks, and significant investment from various entities including tech giants and governments. AI has a historical origin dating back to the 19th century, with a pivotal development in 1950 by Alan Turing's proposition of the Turing test. The recent exponential growth of AI can be attributed to factors such as increased computational power, big data availability, advancements in algorithms like neural networks, and significant investment from tech giants, governments, and startups.", 'The distinctions between artificial narrow intelligence, artificial general intelligence, and artificial superintelligence are discussed, highlighting the current focus on artificial narrow intelligence (weak AI) and the hypothetical nature of artificial superintelligence, with concerns raised by experts about its potential implications for humanity. The discussion encompasses the distinctions between artificial narrow intelligence, artificial general intelligence, and artificial superintelligence, emphasizing the current emphasis on artificial narrow intelligence (weak AI) and the hypothetical nature of artificial superintelligence, with experts expressing concerns about its potential impact on humanity.', 'The interconnectedness of artificial intelligence, machine learning, and deep learning is explained, clarifying that machine learning and deep learning are subsets of AI, providing algorithms and neural networks to solve data-driven problems, while AI encompasses a broad domain including natural language processing, object detection, computer vision, robotics, and expert systems. The interconnectedness of artificial intelligence, machine learning, and deep learning is elucidated, illustrating that machine learning and deep learning serve as subsets of AI, offering algorithms and neural networks to address data-driven problems, while AI encompasses a wide domain including natural language processing, object detection, computer vision, robotics, and expert systems.', 'The machine learning process, involving the stages of defining the objective, data gathering, data preparation, data exploration, model building, model evaluation, and predictions, is detailed, with a practical example provided to illustrate the application of machine learning in solving a specific problem of predicting the occurrence of rain. The machine learning process is delineated, encompassing stages such as defining the objective, data gathering, data preparation, data exploration, model building, model evaluation, and predictions, with a practical example demonstrating the application of machine learning to predict the occurrence of rain.']}, {'end': 6549.537, 'start': 6127.692, 'title': 'Machine learning process overview', 'summary': 'Outlines the steps of the machine learning process, including defining the problem objective, data gathering, data preparation, exploratory data analysis, model building, model evaluation and optimization, and making predictions, emphasizing the importance of understanding data and patterns, and the division of data into training and testing sets.', 'duration': 421.845, 'highlights': ['The most tiresome task in machine learning process is data processing and data cleaning, involving identifying and removing inconsistencies, such as missing values and irrelevant data. Data processing and data cleaning is one of the most time consuming steps in a machine learning process, involving the identification and removal of inconsistencies, such as missing values and irrelevant data.', 'Exploratory data analysis is the most important step in a machine learning process, involving understanding data patterns, drawing insights, and identifying correlations between variables. Exploratory data analysis is the most important step in a machine learning process, involving understanding data patterns, drawing insights, and identifying correlations between variables.', "The training data set is used to build the machine learning model, while the testing data set is used to evaluate the model's performance and efficiency. The training data set is used to build the machine learning model, while the testing data set is used to evaluate the model's performance and efficiency."]}, {'end': 6852.691, 'start': 6550.118, 'title': 'Machine learning types and applications', 'summary': 'Discusses supervised, unsupervised, and reinforcement learning, highlighting the key differences and applications, with supervised learning involving labeled data, unsupervised learning involving unlabeled data and reinforcement learning involving rewards from actions.', 'duration': 302.573, 'highlights': ['Supervised learning involves labeled data and a well-defined training phase, followed by data cleaning, exploratory data analysis, model building, evaluation, and predictions. Supervised learning includes a well-defined training phase using labeled data, followed by data cleaning, exploratory data analysis, model building, evaluation, and predictions.', 'Unsupervised learning involves training with unlabeled data to understand patterns, form clusters based on feature similarity, and classify data without guidance. Unsupervised learning encompasses training with unlabeled data to understand patterns, form clusters based on feature similarity, and classify data without guidance.', 'Reinforcement learning involves an agent performing actions in an environment, learning from observations of rewards, and adapting behavior to maximize rewards. Reinforcement learning entails an agent performing actions in an environment, learning from observations of rewards, and adapting behavior to maximize rewards.']}, {'end': 7076.248, 'start': 6853.231, 'title': 'Reinforcement learning overview', 'summary': 'Explains how reinforcement learning works, its application in advanced machine learning areas, and the differences between supervised, unsupervised, and reinforcement learning, along with the types of problems solved and algorithms used.', 'duration': 223.017, 'highlights': ['Reinforcement learning involves an agent learning by observing and performing actions that result in rewards, mainly used in advanced machine learning areas such as self-driving cars and AlphaGo. Reinforcement learning involves an agent learning through observation and actions to receive rewards, commonly applied in advanced machine learning fields like self-driving cars and AlphaGo.', 'Supervised learning uses labeled data, unsupervised learning uses unlabeled data, and reinforcement learning has no predefined data, requiring the machine to collect and analyze data on its own. Supervised learning relies on labeled data, unsupervised learning operates on unlabeled data, while reinforcement learning lacks predefined data, necessitating the machine to gather and analyze data autonomously.', 'Supervised learning maps labeled input to known output, unsupervised learning discovers patterns and outputs on its own, and reinforcement learning follows a trial and error method. Supervised learning maps labeled input to known output, unsupervised learning discovers patterns and outputs independently, and reinforcement learning employs a trial and error approach.']}], 'duration': 1811.094, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM5265154.jpg', 'highlights': ['The interconnectedness of artificial intelligence, machine learning, and deep learning is explained, clarifying that machine learning and deep learning are subsets of AI, providing algorithms and neural networks to solve data-driven problems, while AI encompasses a broad domain including natural language processing, object detection, computer vision, robotics, and expert systems.', 'The machine learning process, involving the stages of defining the objective, data gathering, data preparation, data exploration, model building, model evaluation, and predictions, is detailed, with a practical example provided to illustrate the application of machine learning in solving a specific problem of predicting the occurrence of rain.', 'Exploratory data analysis is the most important step in a machine learning process, involving understanding data patterns, drawing insights, and identifying correlations between variables.', 'Reinforcement learning involves an agent performing actions in an environment, learning from observations of rewards, and adapting behavior to maximize rewards.', 'Reinforcement learning involves an agent learning by observing and performing actions that result in rewards, mainly used in advanced machine learning areas such as self-driving cars and AlphaGo.']}, {'end': 8982.412, 'segs': [{'end': 7131.45, 'src': 'embed', 'start': 7101.563, 'weight': 0, 'content': [{'end': 7109.524, 'text': "Here again, you'll be using supervised learning classification algorithms such as support vector machines, naive bias, logistic regression and so on.", 'start': 7101.563, 'duration': 7.961}, {'end': 7119.177, 'text': 'Then we have clustering problem and this type of problem involves assigning the input into two or more clusters based on feature similarity.', 'start': 7110.347, 'duration': 8.83}, {'end': 7120.778, 'text': 'For example,', 'start': 7119.797, 'duration': 0.981}, {'end': 7131.45, 'text': 'clustering the viewers into similar groups based on their interest or based on their age or geography can be done by using unsupervised learning algorithms like key means clustering.', 'start': 7120.778, 'duration': 10.672}], 'summary': 'Using supervised learning algorithms such as support vector machines, naive bias, logistic regression, and unsupervised learning algorithms like k-means clustering for classifying and clustering data.', 'duration': 29.887, 'max_score': 7101.563, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM7101563.jpg'}, {'end': 7170.494, 'src': 'embed', 'start': 7142.726, 'weight': 2, 'content': [{'end': 7145.408, 'text': 'Reinforcement learning is something else altogether.', 'start': 7142.726, 'duration': 2.682}, {'end': 7149.911, 'text': 'You can solve reward-based problems and more complex and deep problems.', 'start': 7145.428, 'duration': 4.483}, {'end': 7155.47, 'text': "So now let's move on and understand the different machine learning algorithms.", 'start': 7150.629, 'duration': 4.841}, {'end': 7161.692, 'text': 'Now I will not be going into depth for machine learning algorithms, because there are a lot of algorithms to cover,', 'start': 7156.09, 'duration': 5.602}, {'end': 7165.633, 'text': 'but we have content around almost every machine learning algorithm out there.', 'start': 7161.692, 'duration': 3.941}, {'end': 7170.494, 'text': "So I'm just going to show you a hierarchical diagram of how the algorithms are structured.", 'start': 7166.153, 'duration': 4.341}], 'summary': 'Reinforcement learning can solve reward-based and complex problems. content covers a wide range of machine learning algorithms.', 'duration': 27.768, 'max_score': 7142.726, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM7142726.jpg'}, {'end': 7253.81, 'src': 'embed', 'start': 7222.628, 'weight': 1, 'content': [{'end': 7224.769, 'text': 'All of these are classification algorithms.', 'start': 7222.628, 'duration': 2.141}, {'end': 7229.432, 'text': 'Coming to unsupervised learning, we have clustering and association analysis.', 'start': 7225.329, 'duration': 4.103}, {'end': 7236.916, 'text': 'Clustering problems can be solved by using k-means and association analysis can be solved by using a priori algorithm.', 'start': 7230.192, 'duration': 6.724}, {'end': 7243.522, 'text': 'Apriori algorithm is mainly used in market basket analysis and for this algorithm as well.', 'start': 7237.538, 'duration': 5.984}, {'end': 7245.344, 'text': "I'll be leaving a link in the description.", 'start': 7243.562, 'duration': 1.782}, {'end': 7253.81, 'text': "We've performed a very excellent demo, where in we've showed how market basket analysis can be done by using Apriori algorithm.", 'start': 7245.904, 'duration': 7.906}], 'summary': 'Classification algorithms, unsupervised learning, k-means, apriori algorithm, market basket analysis demo', 'duration': 31.182, 'max_score': 7222.628, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM7222628.jpg'}, {'end': 7293.812, 'src': 'embed', 'start': 7265.713, 'weight': 3, 'content': [{'end': 7270.415, 'text': 'Like I promised earlier, I will be using Python to understand the whole machine learning process.', 'start': 7265.713, 'duration': 4.702}, {'end': 7272.937, 'text': "So let's get started with that demo.", 'start': 7271.196, 'duration': 1.741}, {'end': 7279.64, 'text': "So guys, for those of you who don't know Python, I will leave a couple of links in the description box so that you understand Python.", 'start': 7273.577, 'duration': 6.063}, {'end': 7282.662, 'text': 'But apart from that, Python is pretty understandable.', 'start': 7280.18, 'duration': 2.482}, {'end': 7285.483, 'text': "If you just look at the code, you'll know what exactly I'm talking about.", 'start': 7282.702, 'duration': 2.781}, {'end': 7287.004, 'text': "So don't worry.", 'start': 7286.243, 'duration': 0.761}, {'end': 7289.485, 'text': "And also I'll be explaining everything in the code.", 'start': 7287.384, 'duration': 2.101}, {'end': 7293.812, 'text': "So I'm using PyCharm in order to run the demo.", 'start': 7290.37, 'duration': 3.442}], 'summary': 'Using python to demonstrate machine learning with pycharm, providing helpful links for python beginners.', 'duration': 28.099, 'max_score': 7265.713, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM7265713.jpg'}, {'end': 7343.604, 'src': 'embed', 'start': 7320.255, 'weight': 4, 'content': [{'end': 7328.421, 'text': 'The data set has around 24 features and we will be using 23 features out of that to predict the target variable, which is rain tomorrow.', 'start': 7320.255, 'duration': 8.166}, {'end': 7331.375, 'text': 'So this data set I collected from Kaggle.', 'start': 7329.093, 'duration': 2.282}, {'end': 7336.959, 'text': "For those of you who don't know, Kaggle is an online platform where you can find hundreds of data sets,", 'start': 7331.835, 'duration': 5.124}, {'end': 7341.603, 'text': 'and there are a lot of competitions held by machine learning engineers and all of that.', 'start': 7336.959, 'duration': 4.644}, {'end': 7343.604, 'text': "It's an interesting website.", 'start': 7342.323, 'duration': 1.281}], 'summary': 'Data set from kaggle with 24 features, using 23 to predict rain tomorrow.', 'duration': 23.349, 'max_score': 7320.255, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM7320255.jpg'}, {'end': 7633.838, 'src': 'embed', 'start': 7589.485, 'weight': 5, 'content': [{'end': 7593.768, 'text': "Now after this, we'll just look at any null values and we'll remove them.", 'start': 7589.485, 'duration': 4.283}, {'end': 7597.31, 'text': 'This drop dot any function will just remove all the null values.', 'start': 7594.188, 'duration': 3.122}, {'end': 7605.055, 'text': "Then if you print the shape of your data frame, we'll have around 112,000 rows with 17 variables.", 'start': 7598.11, 'duration': 6.945}, {'end': 7611.819, 'text': 'This is the shape of the data set after removing all the null values and all the redundant or unnecessary variables.', 'start': 7605.675, 'duration': 6.144}, {'end': 7615.165, 'text': "Now it's time to remove the outliers in the data.", 'start': 7612.563, 'duration': 2.602}, {'end': 7620.209, 'text': 'So after you remove any null values, we should also check our dataset for any outliers.', 'start': 7615.665, 'duration': 4.544}, {'end': 7626.213, 'text': 'An outlier is a data point that is very different from your other observations.', 'start': 7620.829, 'duration': 5.384}, {'end': 7631.237, 'text': 'Outliers usually occur because of miscalculations while collecting the data.', 'start': 7627.014, 'duration': 4.223}, {'end': 7633.838, 'text': 'These are some sort of errors in your dataset.', 'start': 7631.817, 'duration': 2.021}], 'summary': 'After removing null values, the dataset contains 112,000 rows with 17 variables. outliers should also be removed.', 'duration': 44.353, 'max_score': 7589.485, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM7589485.jpg'}, {'end': 7707.805, 'src': 'embed', 'start': 7679.303, 'weight': 7, 'content': [{'end': 7685.427, 'text': 'To do this, we can make use of the min max scalar function which Python provides in a package known as SQL learn.', 'start': 7679.303, 'duration': 6.124}, {'end': 7688.888, 'text': 'You can use that package in order to normalize your data set.', 'start': 7686.047, 'duration': 2.841}, {'end': 7693.831, 'text': 'So after normalizing our data set, this is what our data set looks like.', 'start': 7689.869, 'duration': 3.962}, {'end': 7695.995, 'text': 'This is before normalization.', 'start': 7694.534, 'duration': 1.461}, {'end': 7700.399, 'text': 'You can see that these are in two digits whereas these values are in single digits.', 'start': 7696.075, 'duration': 4.324}, {'end': 7702.541, 'text': 'This causes a lot of biasness.', 'start': 7701.019, 'duration': 1.522}, {'end': 7707.805, 'text': 'But once we normalize the values, we know that all of the values are in a similar range.', 'start': 7703.141, 'duration': 4.664}], 'summary': 'Using min max scalar function in sql learn to normalize data resulted in reduced bias in the dataset.', 'duration': 28.502, 'max_score': 7679.303, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM7679303.jpg'}, {'end': 7767.236, 'src': 'embed', 'start': 7740.345, 'weight': 8, 'content': [{'end': 7745.85, 'text': "To do this, we'll be using the select key best function, which is present in the SQL learn library.", 'start': 7740.345, 'duration': 5.505}, {'end': 7755.453, 'text': "There's a predefined function in Python called select K best, which will basically select the most significant predictor variables in our data set.", 'start': 7746.53, 'duration': 8.923}, {'end': 7762.815, 'text': 'when we run that line of code, we get these three variables to be the most significant variables in our data set.', 'start': 7755.453, 'duration': 7.362}, {'end': 7767.236, 'text': 'Now the main aim of this demo is to make you understand how machine learning works.', 'start': 7763.395, 'duration': 3.841}], 'summary': 'Using select k best function in sql learn to select most significant predictor variables and understand machine learning.', 'duration': 26.891, 'max_score': 7740.345, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM7740345.jpg'}, {'end': 8031.037, 'src': 'embed', 'start': 8006.946, 'weight': 10, 'content': [{'end': 8014.049, 'text': 'So guys, all the classification models gave us an accuracy score of approximately 84% to 83%.', 'start': 8006.946, 'duration': 7.103}, {'end': 8017.071, 'text': 'So this is exactly how a machine learning process works.', 'start': 8014.049, 'duration': 3.022}, {'end': 8022.933, 'text': 'You begin by importing all your data, then you perform data pre-processing or data cleaning.', 'start': 8017.751, 'duration': 5.182}, {'end': 8031.037, 'text': 'After that, you perform exploratory data analysis where you understand the important patterns or the important variables in your data set.', 'start': 8023.413, 'duration': 7.624}], 'summary': 'Classification models achieved 83-84% accuracy; process involves data import, cleaning, and exploratory analysis.', 'duration': 24.091, 'max_score': 8006.946, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM8006946.jpg'}, {'end': 8127.986, 'src': 'embed', 'start': 8098.446, 'weight': 13, 'content': [{'end': 8103.889, 'text': 'Another limitation is that it cannot be used in image recognition and object detection,', 'start': 8098.446, 'duration': 5.443}, {'end': 8108.771, 'text': 'because these applications require the implementation of high dimensional data.', 'start': 8103.889, 'duration': 4.882}, {'end': 8118.541, 'text': 'Another major challenge in machine learning is to tell the machine what are the important features it should look for in order to precisely predict the outcome.', 'start': 8109.435, 'duration': 9.106}, {'end': 8119.641, 'text': 'So, basically,', 'start': 8119.021, 'duration': 0.62}, {'end': 8127.986, 'text': "you're selecting the important features for the machine learning model and you're telling them like these are the important features and this is what you should use in order to build the model.", 'start': 8119.641, 'duration': 8.345}], 'summary': 'Challenges in machine learning: limitations in image recognition, object detection, and feature selection for precise prediction.', 'duration': 29.54, 'max_score': 8098.446, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM8098446.jpg'}, {'end': 8199.846, 'src': 'embed', 'start': 8173.769, 'weight': 11, 'content': [{'end': 8178.796, 'text': "So let's understand what deep learning is and why we have deep learning in the first place.", 'start': 8173.769, 'duration': 5.027}, {'end': 8185.907, 'text': 'So deep learning is actually one of the only methods by which we can overcome the challenge of feature extraction.', 'start': 8179.678, 'duration': 6.229}, {'end': 8195.222, 'text': 'This is because deep learning models are capable of learning to focus on the right features by themselves requiring minimal human intervention.', 'start': 8186.536, 'duration': 8.686}, {'end': 8199.846, 'text': 'Meaning that feature extraction will be performed by the deep learning model itself.', 'start': 8195.763, 'duration': 4.083}], 'summary': 'Deep learning enables autonomous feature extraction, reducing human intervention.', 'duration': 26.077, 'max_score': 8173.769, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM8173769.jpg'}, {'end': 8242.284, 'src': 'embed', 'start': 8214.841, 'weight': 12, 'content': [{'end': 8219.084, 'text': 'Also deep learning is mainly used to deal with high dimensional data.', 'start': 8214.841, 'duration': 4.243}, {'end': 8225.732, 'text': 'It is based on the concept of neural networks and is often used in object detection and image processing.', 'start': 8219.145, 'duration': 6.587}, {'end': 8228.214, 'text': 'This is exactly why we need deep learning.', 'start': 8226.252, 'duration': 1.962}, {'end': 8233.86, 'text': 'It solves the problem of processing high dimensional data and manual feature extraction.', 'start': 8228.834, 'duration': 5.026}, {'end': 8242.284, 'text': 'Now how exactly does deep learning work? Now deep learning mimics the basic component of the human brain called the brain cell.', 'start': 8234.52, 'duration': 7.764}], 'summary': 'Deep learning efficiently processes high-dimensional data, mimicking human brain cells.', 'duration': 27.443, 'max_score': 8214.841, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM8214841.jpg'}, {'end': 8434.315, 'src': 'embed', 'start': 8383.523, 'weight': 14, 'content': [{'end': 8391.147, 'text': 'So guys, deep learning is used in highly computational use cases such as face verification, self-driving cars, and so on.', 'start': 8383.523, 'duration': 7.624}, {'end': 8396.609, 'text': "So let's understand the importance of deep learning by looking at a real-world use case.", 'start': 8391.927, 'duration': 4.682}, {'end': 8399.891, 'text': "So I'm sure all of you have heard of the company PayPal.", 'start': 8397.389, 'duration': 2.502}, {'end': 8406.837, 'text': 'Now PayPal makes use of deep learning to identify any possible fraudulent activities.', 'start': 8400.532, 'duration': 6.305}, {'end': 8410.781, 'text': 'So the company makes use of deep learning for fraud detection.', 'start': 8407.578, 'duration': 3.203}, {'end': 8419.508, 'text': 'Now PayPal recently processed over $235 billion in payments from four billion transactions by its more than 170 million customers.', 'start': 8411.401, 'duration': 8.107}, {'end': 8427.754, 'text': 'So basically process this much data by using deep learning.', 'start': 8424.093, 'duration': 3.661}, {'end': 8434.315, 'text': 'PayPal uses machine learning and deep learning algorithms to mine data from the customers purchasing history,', 'start': 8427.754, 'duration': 6.561}], 'summary': 'Paypal uses deep learning for fraud detection, processing $235b payments from 4b transactions by 170m customers.', 'duration': 50.792, 'max_score': 8383.523, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM8383522.jpg'}], 'start': 7082.259, 'title': 'Machine learning fundamentals', 'summary': "Covers types of machine learning problems such as supervised learning for regression and classification, unsupervised learning for clustering, and reinforcement learning. it also demonstrates rain prediction with machine learning using a dataset of around 145,000 observations and 24 features, explores the machine learning process, and discusses the limitations of machine learning including overcoming these with deep learning. additionally, it highlights the use of deep learning in real-world applications, specifically in paypal's fraud detection system, processing over $235 billion in payments from four billion transactions.", 'chapters': [{'end': 7301.316, 'start': 7082.259, 'title': 'Types of machine learning problems', 'summary': 'Covers supervised learning for regression and classification problems, unsupervised learning for clustering, and reinforcement learning. it also mentions specific algorithms such as linear regression, support vector machines, k-means, and apriori algorithm for association analysis.', 'duration': 219.057, 'highlights': ['Supervised learning involves solving regression and classification problems, using algorithms such as linear regression, decision trees, random forests, k-nearest neighbor, logistic regression, naive bias, and support vector machines. Supervised learning addresses regression and classification problems, employing diverse algorithms like k-nearest neighbor and support vector machines.', 'Unsupervised learning includes solving clustering problems using the k-means algorithm and association analysis using the Apriori algorithm. Unsupervised learning encompasses clustering problems, employing the k-means algorithm, and association analysis using the Apriori algorithm.', 'Reinforcement learning is distinct and applies to reward-based problems and more complex scenarios. Reinforcement learning is utilized for reward-based and complex problems.', 'Python is used to demonstrate the machine learning process, with resources provided for learning Python and running the demo using PyCharm. Python is employed to demonstrate the machine learning process, along with resources for learning Python and running the demo using PyCharm.']}, {'end': 7718.674, 'start': 7301.836, 'title': 'Rain prediction with machine learning', 'summary': 'Demonstrates the process of building a machine learning model to predict whether it will rain tomorrow using a dataset of around 145,000 observations and 24 features, by assessing null values, removing unnecessary variables, handling outliers, and normalizing the data.', 'duration': 416.838, 'highlights': ["The dataset contains around 145,000 observations and 24 features, with 23 features being used to predict the target variable, which is rain tomorrow. The dataset consists of around 145,000 observations and 24 features, out of which 23 features are used for predicting the target variable, 'rain tomorrow.'", 'Checking for null values reveals that the first four columns have more than 40% null values, prompting the removal of unnecessary variables to aid in prediction. The process of checking for null values indicates that the first four columns have more than 40% null values, emphasizing the need to remove unnecessary variables to enhance prediction accuracy.', 'After removing null values and unnecessary variables, the data set comprises around 112,000 rows with 17 variables. Following the removal of null values and unnecessary variables, the data set consists of approximately 112,000 rows and 17 variables.', "The process involves removing outliers from the dataset to improve the accuracy of the machine learning model. Removing outliers is a crucial step in improving the accuracy of the machine learning model by ensuring the dataset's integrity.", 'Normalizing the dataset is performed using the min-max scalar function to avoid biasness in the output. Normalization of the dataset is essential to prevent biasness in the output, achieved using the min-max scalar function to ensure all values are within a similar range.']}, {'end': 8052.305, 'start': 7719.508, 'title': 'Machine learning process', 'summary': 'Covers the process of exploratory data analysis, including the use of select k best function to identify significant predictor variables, followed by the application of classification algorithms such as logistic regression, random forest classifier, decision tree classifier, and support vector machine, resulting in approximately 84% accuracy for each algorithm.', 'duration': 332.797, 'highlights': ['The chapter covers the process of exploratory data analysis, including the use of select K best function to identify significant predictor variables This highlights the initial step of the process, emphasizing the identification of significant predictor variables using the select K best function.', 'Application of classification algorithms such as logistic regression, random forest classifier, decision tree classifier, and support vector machine This highlights the variety of classification algorithms used, showcasing the diversity of approaches employed in the analysis.', 'Resulting in approximately 84% accuracy for each algorithm This highlights the key quantifiable outcome of the analysis, indicating the high level of accuracy achieved across all algorithms.']}, {'end': 8382.924, 'start': 8053.105, 'title': 'Limitations of machine learning', 'summary': 'Discusses the limitations of machine learning, including handling high dimensional data, manual feature extraction, and its inability to be used in image recognition and object detection. it also explains how deep learning overcomes these limitations by automatically performing feature extraction and processing high dimensional data.', 'duration': 329.819, 'highlights': ['Deep learning automatically performs feature extraction, solving the challenge of manually selecting important features in machine learning. Deep learning models are capable of learning to focus on the right features by themselves, requiring minimal human intervention, which solves the manual feature extraction challenge in machine learning.', 'Deep learning is used to process high dimensional data and is applied in image recognition and object detection, addressing the limitations of machine learning in handling such data. Deep learning is mainly used to deal with high dimensional data and is often used in object detection and image processing, which solves the problem of processing high dimensional data and the inability of machine learning to be used in image recognition and object detection.', 'Machine learning is incapable of handling high dimensional data and cannot be used in image recognition and object detection, which led to the concept of deep learning. Machine learning algorithms and models are not capable of handling high dimensional data, and it cannot be used in image recognition and object detection, leading to the rise of the concept of deep learning.']}, {'end': 8982.412, 'start': 8383.523, 'title': 'Importance of deep learning in real-world use cases', 'summary': "Highlights the use of deep learning in real-world applications, specifically in paypal's fraud detection system, processing over $235 billion in payments from four billion transactions, and the significance of deep learning in identifying patterns of fraudulent activity.", 'duration': 598.889, 'highlights': ["PayPal processed over $235 billion in payments from four billion transactions by its more than 170 million customers using deep learning for fraud detection. PayPal's use of deep learning for fraud detection is highlighted, processing a significant amount of transaction data to identify fraudulent activities.", "PayPal has been relying on deep learning and machine learning technology for around 10 years, switching from simple linear models to more advanced machine learning technology called deep learning. PayPal's transition from simple linear models to advanced machine learning technology, specifically deep learning, showcases the increasing reliance on more advanced technology for fraud detection.", "Facebook also utilizes deep learning technology for face verification in features such as tagging friends in photos. The application of deep learning in Facebook's face verification feature is mentioned, demonstrating the widespread usage of deep learning in real-world scenarios."]}], 'duration': 1900.153, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM7082259.jpg', 'highlights': ['Supervised learning addresses regression and classification problems, employing diverse algorithms like k-nearest neighbor and support vector machines.', 'Unsupervised learning encompasses clustering problems, employing the k-means algorithm, and association analysis using the Apriori algorithm.', 'Reinforcement learning is utilized for reward-based and complex problems.', 'Python is employed to demonstrate the machine learning process, along with resources for learning Python and running the demo using PyCharm.', "The dataset consists of around 145,000 observations and 24 features, out of which 23 features are used for predicting the target variable, 'rain tomorrow.'", 'Following the removal of null values and unnecessary variables, the data set consists of approximately 112,000 rows and 17 variables.', "Removing outliers is a crucial step in improving the accuracy of the machine learning model by ensuring the dataset's integrity.", 'Normalization of the dataset is essential to prevent biasness in the output, achieved using the min-max scalar function to ensure all values are within a similar range.', 'This highlights the initial step of the process, emphasizing the identification of significant predictor variables using the select K best function.', 'This highlights the variety of classification algorithms used, showcasing the diversity of approaches employed in the analysis.', 'This highlights the key quantifiable outcome of the analysis, indicating the high level of accuracy achieved across all algorithms.', 'Deep learning models are capable of learning to focus on the right features by themselves, requiring minimal human intervention, which solves the manual feature extraction challenge in machine learning.', 'Deep learning is mainly used to deal with high dimensional data and is often used in object detection and image processing, which solves the problem of processing high dimensional data and the inability of machine learning to be used in image recognition and object detection.', 'Machine learning algorithms and models are not capable of handling high dimensional data, and it cannot be used in image recognition and object detection, leading to the rise of the concept of deep learning.', "PayPal's use of deep learning for fraud detection is highlighted, processing a significant amount of transaction data to identify fraudulent activities.", "PayPal's transition from simple linear models to advanced machine learning technology, specifically deep learning, showcases the increasing reliance on more advanced technology for fraud detection.", "The application of deep learning in Facebook's face verification feature is mentioned, demonstrating the widespread usage of deep learning in real-world scenarios."]}, {'end': 12192.972, 'segs': [{'end': 9057.793, 'src': 'embed', 'start': 9026.409, 'weight': 0, 'content': [{'end': 9030.112, 'text': 'Now before that, let me just tell you something about our data set.', 'start': 9026.409, 'duration': 3.703}, {'end': 9037.797, 'text': 'The data set contains transactions made by credit cards in the year September 2013 by European cardholders.', 'start': 9030.272, 'duration': 7.525}, {'end': 9044.342, 'text': 'This data set presents transactions that occurred in two days where we have 492 frauds out of 285000 transactions.', 'start': 9038.398, 'duration': 5.944}, {'end': 9051.508, 'text': 'approximately 285,000 transactions.', 'start': 9048.826, 'duration': 2.682}, {'end': 9057.793, 'text': 'Out of these transactions, 492 were frauds and the data set is quite unbalanced.', 'start': 9052.049, 'duration': 5.744}], 'summary': 'The data set contains 285,000 credit card transactions, with 492 frauds, making it unbalanced.', 'duration': 31.384, 'max_score': 9026.409, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM9026409.jpg'}, {'end': 9203.566, 'src': 'embed', 'start': 9177.37, 'weight': 1, 'content': [{'end': 9185.733, 'text': 'So we have around 492 fraudulent transactions and around two hundred and eighty four thousand three hundred and fifteen non fraudulent transactions.', 'start': 9177.37, 'duration': 8.363}, {'end': 9189.798, 'text': 'So when you see this, you know that our data set is highly unbalanced.', 'start': 9186.536, 'duration': 3.262}, {'end': 9195.561, 'text': 'Highly unbalanced means that one class has a really small number when compared to the other class.', 'start': 9190.258, 'duration': 5.303}, {'end': 9197.782, 'text': "There's no balance between the two classes.", 'start': 9195.981, 'duration': 1.801}, {'end': 9203.566, 'text': "So here what we're doing is we are sorting the data set by class for stratified sampling.", 'start': 9198.603, 'duration': 4.963}], 'summary': 'Data set has 492 fraudulent and 284,315 non fraudulent transactions, highly unbalanced, requiring stratified sampling.', 'duration': 26.196, 'max_score': 9177.37, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM9177370.jpg'}, {'end': 9887.943, 'src': 'embed', 'start': 9862.472, 'weight': 2, 'content': [{'end': 9868.899, 'text': 'out of all the data that we generate, only 21% of the data is structured and well formatted, right.', 'start': 9862.472, 'duration': 6.427}, {'end': 9877.308, 'text': 'the remaining of the data is unstructured, and the major sources of unstructured data include text messages from WhatsApp, Facebook likes,', 'start': 9868.899, 'duration': 8.409}, {'end': 9880.332, 'text': 'comments on Instagram, the bulk emails and all of this.', 'start': 9877.308, 'duration': 3.024}, {'end': 9884.381, 'text': 'All of this accounts for the unstructured data that we have today.', 'start': 9881.139, 'duration': 3.242}, {'end': 9887.943, 'text': 'Now the data we generate is used to grow a business.', 'start': 9884.881, 'duration': 3.062}], 'summary': 'Only 21% of generated data is structured, while the rest is unstructured, derived from sources like whatsapp, facebook, and instagram.', 'duration': 25.471, 'max_score': 9862.472, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM9862472.jpg'}, {'end': 10004.697, 'src': 'embed', 'start': 9976.685, 'weight': 3, 'content': [{'end': 9982.927, 'text': 'So predictive typing and spell checkers all of these are applications of natural language processing.', 'start': 9976.685, 'duration': 6.242}, {'end': 9991.911, 'text': 'all of this basically involves processing the natural language that we use and deriving some useful information from it right or running businesses from it.', 'start': 9982.927, 'duration': 8.984}, {'end': 9996.353, 'text': 'Netflix uses natural language processing in a really good, fashioned way.', 'start': 9991.911, 'duration': 4.442}, {'end': 10004.697, 'text': 'It basically studies the reviews that customer gives for a particular movie and it tries to figure out if that movie is good or bad,', 'start': 9997.133, 'duration': 7.564}], 'summary': 'Natural language processing applied in predictive typing, spell checkers, and by netflix to analyze customer reviews.', 'duration': 28.012, 'max_score': 9976.685, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM9976685.jpg'}, {'end': 10613.382, 'src': 'embed', 'start': 10590.128, 'weight': 4, 'content': [{'end': 10597.114, 'text': 'they can generate their features on which the outcome will depend on, and, at the same time, it also solves the dimensionality problem as well.', 'start': 10590.128, 'duration': 6.986}, {'end': 10601.237, 'text': 'If you have very large number of inputs and outputs you can make use of a deep learning algorithm.', 'start': 10597.554, 'duration': 3.683}, {'end': 10603.439, 'text': 'Now, what exactly is deep learning?', 'start': 10601.617, 'duration': 1.822}, {'end': 10610.12, 'text': 'again?. Since we know that it has been evolved by machine learning, and machine learning is nothing but a subset of artificial intelligence,', 'start': 10603.439, 'duration': 6.681}, {'end': 10613.382, 'text': 'and the idea behind artificial intelligence is to imitate the human behavior.', 'start': 10610.12, 'duration': 3.262}], 'summary': 'Deep learning addresses dimensionality problem with large input and output data, evolving from machine learning to imitate human behavior.', 'duration': 23.254, 'max_score': 10590.128, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM10590128.jpg'}, {'end': 10851.582, 'src': 'embed', 'start': 10822.692, 'weight': 5, 'content': [{'end': 10824.873, 'text': 'So this is how a deep network looks like.', 'start': 10822.692, 'duration': 2.181}, {'end': 10826.994, 'text': 'Now there can be multiple hidden layers.', 'start': 10825.374, 'duration': 1.62}, {'end': 10828.875, 'text': 'There can be hundreds of hidden layers as well.', 'start': 10827.054, 'duration': 1.821}, {'end': 10832.397, 'text': 'But when we talk about machine learning that was not the case.', 'start': 10829.355, 'duration': 3.042}, {'end': 10836.918, 'text': 'We were not able to process multiple hidden layers when we talk about machine learning.', 'start': 10832.777, 'duration': 4.141}, {'end': 10840.36, 'text': 'So because of deep learning we have multiple hidden layers at once.', 'start': 10837.299, 'duration': 3.061}, {'end': 10842.913, 'text': 'Now let us understand this with an example.', 'start': 10841.291, 'duration': 1.622}, {'end': 10845.275, 'text': "So we'll take an image which has four pixels.", 'start': 10843.353, 'duration': 1.922}, {'end': 10851.582, 'text': "So, if you can notice, we have four pixels here, among which the top two pixels are bright, that is, they're black in color,", 'start': 10845.635, 'duration': 5.947}], 'summary': 'Deep networks can have hundreds of hidden layers, unlike traditional machine learning, enabling more complex processing.', 'duration': 28.89, 'max_score': 10822.692, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM10822692.jpg'}, {'end': 11097.795, 'src': 'embed', 'start': 11066.752, 'weight': 6, 'content': [{'end': 11071.113, 'text': 'And then we accumulate these features for the correct face and then we determine the image.', 'start': 11066.752, 'duration': 4.361}, {'end': 11075.334, 'text': 'So this is how a deep learning network or you can say deep network looks like.', 'start': 11071.653, 'duration': 3.681}, {'end': 11078.215, 'text': "And I'll give you some applications of deep learning.", 'start': 11076.214, 'duration': 2.001}, {'end': 11082.058, 'text': 'So here are the applications of deep learning it can be used in self-driving cars.', 'start': 11078.755, 'duration': 3.303}, {'end': 11084, 'text': 'So you must have heard about self-driving cars.', 'start': 11082.439, 'duration': 1.561}, {'end': 11086.483, 'text': 'So what happens it will capture the images around it.', 'start': 11084.041, 'duration': 2.442}, {'end': 11092.149, 'text': 'It will process that huge amount of data and then it will decide what action should it take to take left right should it stop.', 'start': 11086.503, 'duration': 5.646}, {'end': 11097.795, 'text': 'So accordingly it will decide what action should it take and that will reduce the amount of accidents that happens every year.', 'start': 11092.669, 'duration': 5.126}], 'summary': 'Deep learning can reduce accidents in self-driving cars by processing image data to make decisions, potentially decreasing annual accident rates.', 'duration': 31.043, 'max_score': 11066.752, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM11066752.jpg'}, {'end': 11577.044, 'src': 'embed', 'start': 11544.371, 'weight': 8, 'content': [{'end': 11548.632, 'text': 'Okay, so these are some of the applications of using object detection in real life.', 'start': 11544.371, 'duration': 4.261}, {'end': 11552.673, 'text': 'Now coming to this object detection.', 'start': 11549.672, 'duration': 3.001}, {'end': 11559.715, 'text': "let's look at the typical workflow and how does it work when we want to build an object detection algorithm using TensorFlow.", 'start': 11552.673, 'duration': 7.042}, {'end': 11567.954, 'text': 'When we are working with the applications like object detection, First we need to prepare that training data.', 'start': 11561.475, 'duration': 6.479}, {'end': 11577.044, 'text': "I'll have to build a training data to such that to identify whether my given image has the required number of classes.", 'start': 11568.955, 'duration': 8.089}], 'summary': 'Object detection in real life and building algorithm using tensorflow for identifying classes in training data.', 'duration': 32.673, 'max_score': 11544.371, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM11544371.jpg'}, {'end': 11712.691, 'src': 'embed', 'start': 11686.392, 'weight': 7, 'content': [{'end': 11695.678, 'text': 'my deep learning model will be able to identify what is present in the image and also it also give tells me as where exactly that object is present in a given image,', 'start': 11686.392, 'duration': 9.286}, {'end': 11699.601, 'text': "and that's how an object detection machine learning algorithm would work.", 'start': 11695.678, 'duration': 3.923}, {'end': 11705.608, 'text': 'Now in order to build this deep learning models, which is capable of performing object detection.', 'start': 11700.825, 'duration': 4.783}, {'end': 11712.691, 'text': 'We have various frameworks now one of the common framework that we would use is the tensorflow.', 'start': 11706.128, 'duration': 6.563}], 'summary': 'Deep learning model identifies objects in images using tensorflow framework.', 'duration': 26.299, 'max_score': 11686.392, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM11686392.jpg'}, {'end': 11862.161, 'src': 'embed', 'start': 11829.917, 'weight': 9, 'content': [{'end': 11838.002, 'text': 'So, just like we have a course in CPU, will also have the processing course in GPU and will be utilized those GPU cores,', 'start': 11829.917, 'duration': 8.085}, {'end': 11842.906, 'text': 'and we have seen that we could get around 10 to 20 X faster than we could.', 'start': 11838.002, 'duration': 4.904}, {'end': 11845.647, 'text': 'then what we could achieve in our CPU.', 'start': 11842.906, 'duration': 2.741}, {'end': 11851.677, 'text': "That's the advantage of working with deep learning frameworks and creating the data as tensors.", 'start': 11846.795, 'duration': 4.882}, {'end': 11862.161, 'text': 'And if I have a CUDA supported GPU, then I can also make use of this GPU course to get 10 to 20 times of faster data processing compared to CPU.', 'start': 11852.197, 'duration': 9.964}], 'summary': 'Utilizing gpu cores can achieve 10-20x faster data processing than cpu for deep learning with cuda supported gpu.', 'duration': 32.244, 'max_score': 11829.917, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM11829917.jpg'}, {'end': 12145.642, 'src': 'embed', 'start': 12119.085, 'weight': 10, 'content': [{'end': 12128.572, 'text': 'Okay, so Yolo is one of the most commonly used object detection model when it comes to object performing the object detection using deep learning models.', 'start': 12119.085, 'duration': 9.487}, {'end': 12133.256, 'text': "So it's an object detection models will use this already trained Yolo model.", 'start': 12128.913, 'duration': 4.343}, {'end': 12140.78, 'text': "and we'll send in our data and we'll see how the output would look like when I perform object detection with this YOLO.", 'start': 12133.857, 'duration': 6.923}, {'end': 12145.642, 'text': "Okay, so let's go back to our Google Colab.", 'start': 12142.801, 'duration': 2.841}], 'summary': 'Yolo is a commonly used object detection model for deep learning, showing how to use it for object detection and data output.', 'duration': 26.557, 'max_score': 12119.085, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM12119085.jpg'}], 'start': 8983.173, 'title': 'Implementing deep learning for practical applications', 'summary': 'Demonstrates a practical implementation of deep learning for credit card fraud detection achieving over 90% accuracy with a dataset of 285,000 transactions, discusses natural language processing applications due to 2.5 quintillion bytes of unstructured data generated daily, explores the limitations of machine learning and presents deep learning as a solution, explains deep learning process and its applications, discusses object detection applications including covid-19 protocol adherence checking and explains tensorflow advantages and a hands-on demo of object detection using the yolo model.', 'chapters': [{'end': 9782.735, 'start': 8983.173, 'title': 'Credit card fraud detection', 'summary': 'Demonstrates a practical implementation of deep learning to construct a high-performance model for credit card fraud detection using a dataset of 285,000 transactions, of which 492 were fraudulent, achieving a balanced dataset through data preprocessing and achieving an accuracy of over 90% in the training phase.', 'duration': 799.562, 'highlights': ['The dataset contains 285,000 transactions, with 492 being frauds, resulting in an unbalanced class distribution of 0.172% for fraudulent transactions. Provides key information about the dataset, including the total number of transactions and the imbalance in the class distribution.', 'Performed data preprocessing to balance the dataset, resulting in 2,508 non-fraudulent and 492 fraudulent transactions, and used stratified sampling to handle the unbalanced dataset. Describes the technique used to balance the dataset and the resulting distribution of transactions.', 'Built a deep learning model with three fully connected layers, utilized dropout technique to prevent overfitting, and achieved an accuracy of over 90% in the training phase. Summarizes the key steps in building the deep learning model and the achieved accuracy.']}, {'end': 10465.105, 'start': 9783.275, 'title': 'Natural language processing', 'summary': 'Discusses the need for natural language processing due to the massive unstructured data being generated, with 2.5 quintillion bytes created daily, and its applications such as autocomplete, spam detection, sentimental analysis, chatbots, machine translation, and the basic terminologies under natural language processing.', 'duration': 681.83, 'highlights': ['2.5 quintillion bytes of data are created daily, with 79% being unstructured data. It is a known fact that we are creating 2.5 quintillion bytes of data every day, and only 21% of the data is structured and well formatted, with the remaining 79% being unstructured data.', '1.7 million pictures are posted on Instagram per minute, and 347 thousand tweets are sent per minute. The amount of data generated per minute includes around 1.7 million pictures posted on Instagram and 347 thousand tweets sent per minute.', 'Applications of natural language processing include autocomplete, spam detection, sentimental analysis, chatbots, and machine translation. Various applications of natural language processing include autocomplete, spam detection, sentimental analysis, chatbots, and machine translation for analyzing social media content, converting human language into desirable actions, and translating text to another language.', 'Key terminologies under natural language processing include tokenization, stemming, lemmatization, and stop words. The basic terminologies under natural language processing include tokenization, stemming, lemmatization, and stop words, which are essential for breaking down data into smaller chunks, simplifying word analysis, and focusing on important keywords.', 'Document term matrix is used to show the frequency of words in a particular document. The document term matrix (DTM) is a matrix that displays the frequency of words in a specific document, aiding in the analysis and understanding of the distribution of words in the document.']}, {'end': 10842.913, 'start': 10465.856, 'title': 'Limitations of machine learning and solution with deep learning', 'summary': 'Explores the limitations of machine learning, including high dimensionality of data and the inability to solve complex ai problems, and presents deep learning as a solution, emphasizing its capability to focus on the right features by itself and to solve the dimensionality problem.', 'duration': 377.057, 'highlights': ['Deep learning models can focus on the right features by themselves, requiring little guidance from the programmer, and can solve the dimensionality problem as well, making them suitable for dealing with a large number of inputs and outputs.', 'Machine learning algorithms fail to deal with high dimensionality of data, leading to inefficiency in solving complex problems such as object recognition or handwriting recognition.', 'The complex problems such as object recognition or handwriting recognition become a huge challenge for machine learning algorithms to solve.', 'Deep networks are neural networks with multiple hidden layers, enabling the processing of multiple hidden layers at once, which was not possible in traditional machine learning.']}, {'end': 11355.103, 'start': 10843.353, 'title': 'Deep learning overview', 'summary': 'Explains the process of deep learning, including the division of pixels, weight assignment, and image transformation, as well as its applications in self-driving cars, voice control assistance, image caption generation, and machine translation. it also details the concept and examples of object detection.', 'duration': 511.75, 'highlights': ['The process of deep learning involves dividing pixels and sending them to nodes with random weights, resulting in image transformation. The division of pixels and assignment of random weights to nodes leads to image transformation, demonstrating the workings of deep learning.', 'Deep learning applications include self-driving cars, voice control assistance, automatic image caption generation, and automatic machine translation. Deep learning finds applications in diverse areas such as self-driving cars, voice control assistance, image caption generation, and machine translation.', 'Object detection involves identifying objects in images and determining their location, illustrated by the example of Facebook photo tagging. Object detection encompasses identifying objects in images and locating them, as exemplified by the Facebook photo tagging feature.']}, {'end': 11705.608, 'start': 11355.563, 'title': 'Object detection applications', 'summary': "Discusses the applications of object detection, including face recognition, people counting, covid-19 protocol adherence checking, industrial quality control, self-driving cars, security, and real-life examples such as mahindra xuv 700's object detection functionality. it also explains the typical workflow of building an object detection algorithm using tensorflow and the process of training and testing the model.", 'duration': 350.045, 'highlights': ["Object detection applications including face recognition, people counting, COVID-19 protocol adherence checking, industrial quality control, self-driving cars, security, and real-life examples such as Mahindra XUV 700's object detection functionality. The chapter extensively covers various real-life applications of object detection, providing a comprehensive overview of its practical uses.", 'Explanation of the typical workflow of building an object detection algorithm using TensorFlow, including the process of preparing training data, building a classifier, training the model, and testing the model. The chapter provides a detailed explanation of the workflow involved in building an object detection algorithm using TensorFlow, outlining the key steps from preparing training data to testing the model.', "Description of the machine learning model's ability to identify what is present in an image and locate the object's position, showcasing the dual functionality of the model. The chapter highlights the dual functionality of the machine learning model in identifying objects and determining their precise locations within images, emphasizing its comprehensive capabilities."]}, {'end': 12192.972, 'start': 11706.128, 'title': 'Tensorflow for deep learning', 'summary': 'Discusses the advantages of using tensorflow for deep learning, including its ease of deployment, the use of tensors for data representation, and the capability to utilize gpu cores for 10 to 20 times faster data processing compared to cpu, along with a hands-on demo of object detection using the yolo model.', 'duration': 486.844, 'highlights': ["TensorFlow's advantage in utilizing GPU cores for 10 to 20 times faster data processing compared to CPU By creating the data inside tensors, it enables the utilization of GPU cores, resulting in 10 to 20 times faster data processing compared to CPU.", "TensorFlow's capability to automatically track data manipulation and update model parameters using data flow graph TensorFlow's data flow graph tracks data manipulation and updates model parameters during the operation, eliminating the need to manually specify equations and gradients.", 'Hands-on demo of object detection using the YOLO model, one of the most commonly used object detection models in deep learning The chapter provides a hands-on demo of object detection using the YOLO model, showcasing the implementation of object detection with TensorFlow.', 'Representation of data in tensors for deep learning and the comparison to numpy array object Tensors, multi-dimensional array objects, are used for representing data in deep learning, similar to numpy array objects, and are essential for processing data with GPU cores.']}], 'duration': 3209.799, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM8983173.jpg', 'highlights': ['Achieved over 90% accuracy in credit card fraud detection with 285,000 transactions', 'Balanced dataset with 2,508 non-fraudulent and 492 fraudulent transactions', '2.5 quintillion bytes of unstructured data generated daily, 79% unstructured', 'Applications of natural language processing include autocomplete, spam detection, and chatbots', 'Deep learning models can handle high dimensionality and solve complex problems', 'Deep networks process multiple hidden layers, unlike traditional machine learning', 'Deep learning applications include self-driving cars and automatic image caption generation', 'Object detection involves identifying objects in images and determining their location', 'Various real-life applications of object detection are covered in the chapter', 'TensorFlow utilizes GPU cores for faster data processing compared to CPU', 'Hands-on demo of object detection using the YOLO model is provided']}, {'end': 14022.036, 'segs': [{'end': 12453.719, 'src': 'embed', 'start': 12424.778, 'weight': 0, 'content': [{'end': 12430.54, 'text': "So it's like 132 times faster than what I have got from my CPU.", 'start': 12424.778, 'duration': 5.762}, {'end': 12437.772, 'text': "So that's the computation speed that we would get when we work with GPUs.", 'start': 12430.82, 'duration': 6.952}, {'end': 12443.375, 'text': 'Okay Now if I see over here, so it says these are the objects that has been identified from a given image.', 'start': 12438.153, 'duration': 5.222}, {'end': 12450.138, 'text': "So there's a giraffe with 100% confidence and there is a zebra with 99% confidence.", 'start': 12443.495, 'duration': 6.643}, {'end': 12453.719, 'text': 'So clearly we would have.', 'start': 12451.659, 'duration': 2.06}], 'summary': 'Gpu computation speed is 132 times faster than cpu. image recognition identified a giraffe with 100% confidence and a zebra with 99% confidence.', 'duration': 28.941, 'max_score': 12424.778, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM12424778.jpg'}, {'end': 12559.06, 'src': 'embed', 'start': 12529.011, 'weight': 1, 'content': [{'end': 12536.807, 'text': 'and if I come down, there is a code which talks about as we can perform the object detection in videos Now, instead of connecting to the videos.', 'start': 12529.011, 'duration': 7.796}, {'end': 12548.814, 'text': 'we can also make use of OpenCV library and you take the starter webcam session and then we can run this object detection on the video file as well.', 'start': 12536.807, 'duration': 12.007}, {'end': 12559.06, 'text': 'Or we can run that object detection on the webcam stream as well to detect what are the objects that are present in the image.', 'start': 12549.954, 'duration': 9.106}], 'summary': 'Using opencv library to perform object detection in videos and webcam streams.', 'duration': 30.049, 'max_score': 12529.011, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM12529011.jpg'}, {'end': 13110.051, 'src': 'embed', 'start': 13080.55, 'weight': 2, 'content': [{'end': 13083.132, 'text': 'Let me give you a quick recap of what happens in convolution layer.', 'start': 13080.55, 'duration': 2.582}, {'end': 13088.856, 'text': 'So, basically, we have taken three features, all right, and one by one will take one feature, move it through the entire image,', 'start': 13083.432, 'duration': 5.424}, {'end': 13094.921, 'text': 'and when we are moving it at that time, we are multiplying the pixel value of the image with that of the corresponding pixel value of the filter,', 'start': 13088.856, 'duration': 6.065}, {'end': 13098.484, 'text': 'adding them up, dividing by the total number of pixels to get the output.', 'start': 13094.921, 'duration': 3.563}, {'end': 13102.387, 'text': 'So when we do that for all the filters we get we got these three outputs.', 'start': 13098.864, 'duration': 3.523}, {'end': 13102.667, 'text': 'All right.', 'start': 13102.447, 'duration': 0.22}, {'end': 13105.449, 'text': "So let's move forward and we'll see what happens in ReLU layer.", 'start': 13103.007, 'duration': 2.442}, {'end': 13110.051, 'text': 'So this is ReLU layer guys and people who have gone through the previous tutorial actually know what it is.', 'start': 13105.809, 'duration': 4.242}], 'summary': 'Explanation of convolution and relu layers in neural networks.', 'duration': 29.501, 'max_score': 13080.55, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM13080550.jpg'}, {'end': 13347.145, 'src': 'embed', 'start': 13321.274, 'weight': 3, 'content': [{'end': 13326.835, 'text': 'we are continuously multiplying the image pixel value with that of the corresponding filter pixel value,', 'start': 13321.274, 'duration': 5.561}, {'end': 13328.876, 'text': 'and then we were dividing it by the total number of pixels.', 'start': 13326.835, 'duration': 2.041}, {'end': 13329.916, 'text': 'All right.', 'start': 13329.276, 'duration': 0.64}, {'end': 13333.337, 'text': 'with that we got three output after passing through the convolution layer.', 'start': 13329.916, 'duration': 3.421}, {'end': 13337.778, 'text': 'then those three output we pass through a relu layer where we have removed the negative value.', 'start': 13333.337, 'duration': 4.441}, {'end': 13343.722, 'text': 'All right, and after removing negative value again, we have got the three outputs then those three outputs we pass through pooling layer.', 'start': 13338.157, 'duration': 5.565}, {'end': 13347.145, 'text': "So, basically, we're trying to shrink our image and what we did.", 'start': 13343.742, 'duration': 3.403}], 'summary': 'Image processing involves multiplying, dividing, and passing through layers to obtain three outputs.', 'duration': 25.871, 'max_score': 13321.274, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM13321274.jpg'}, {'end': 13548.603, 'src': 'embed', 'start': 13521.667, 'weight': 4, 'content': [{'end': 13524.849, 'text': "And now I'm going to sum the corresponding values of my input image vector as well.", 'start': 13521.667, 'duration': 3.182}, {'end': 13533.952, 'text': 'So the first value is .9, then the fourth value is .87, fifth value is .96, 10th value is .89, and the 11th value is .94.', 'start': 13525.209, 'duration': 8.743}, {'end': 13536.854, 'text': "So after doing the sum of these values, I've got 4.56.", 'start': 13533.952, 'duration': 2.902}, {'end': 13543.258, 'text': "When I divide this by five, I've got .91, right? Now this is for X now when I do the same process for O.", 'start': 13536.854, 'duration': 6.404}, {'end': 13548.603, 'text': 'So, you know, if you notice I have second third ninth and twelfth element vector values is high.', 'start': 13543.258, 'duration': 5.345}], 'summary': 'Summed input image vector values: 4.56, x: .91, o: high element vectors', 'duration': 26.936, 'max_score': 13521.667, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM13521667.jpg'}], 'start': 12193.268, 'title': 'Object detection and cnn basics', 'summary': 'Covers object detection in tensorflow with gpu, achieving a 132 times speed boost compared to cpu. it also explains the use of convolutional neural networks for object detection in videos, and achieves an 88% accuracy in banknote authentication using tensorflow.', 'chapters': [{'end': 12529.011, 'start': 12193.268, 'title': 'Object detection with gpu in tensorflow', 'summary': 'Demonstrates the process of using pre-trained weights for object detection in tensorflow, highlighting the significant increase in speed when running on a gpu, with a performance boost of 132 times compared to cpu, and the impact of adjusting threshold values on object detection.', 'duration': 335.743, 'highlights': ['Using GPU leads to 132 times faster computation speed compared to CPU for object detection in TensorFlow. Running the object detection model on GPU took around 165 milliseconds, 132 times faster than the 21,915 milliseconds taken on CPU.', 'Adjusting the threshold value impacts the number of objects detected in an image when using TensorFlow for object detection. By specifying a threshold of 98%, objects with confidence lower than 98% were omitted from detection, while reducing the threshold value led to the addition of more detected objects.', "The process involves obtaining pre-trained weights from a GitHub repository and testing the object detector with specific images. The speaker obtained pre-trained weights from a GitHub repository, tested the object detector with images such as 'persons image' and 'giraffe.jpg', and measured the computation speed on CPU and GPU."]}, {'end': 12800.184, 'start': 12529.011, 'title': 'Object detection and convolutional neural networks', 'summary': 'Discusses object detection in videos using opencv library and yolo object detector, explaining the concept of object detection and the limitations of fully connected networks for image classification, and the need for convolutional neural networks for handling less amount of weights and neurons, inspired by the visual cortex.', 'duration': 271.173, 'highlights': ['The chapter discusses object detection in videos using OpenCV library and YOLO object detector. Object detection in videos using OpenCV library and YOLO object detector is explained for performing object detection on video files and webcam streams.', 'Explaining the concept of object detection and the limitations of fully connected networks for image classification. The concept of object detection is explained, including the detection of object presence and identity, and the limitations of fully connected networks for image classification due to the large number of weights and potential overfitting.', "The need for convolutional neural networks for handling less amount of weights and neurons, inspired by the visual cortex. The need for convolutional neural networks is explained, emphasizing their ability to handle fewer weights and neurons, inspired by the visual cortex's specific region sensitivity."]}, {'end': 13131.583, 'start': 12800.465, 'title': 'Convolutional neural network basics', 'summary': 'Explains how a convolutional neural network works, with three layers - convolutional layer, relu layer, pooling layer and fully connected layer, using an example of image classification and demonstrating the process of comparing features, multiplying pixel values, and adding to obtain outputs.', 'duration': 331.118, 'highlights': ['The process of comparing features, multiplying pixel values, and adding to obtain outputs in the convolution layer The chapter explains the process of comparing features, multiplying pixel values, adding them up, dividing by the total number of pixels to obtain outputs in the convolution layer, demonstrating the application of three different filters on an image for classification.', 'Explanation of ReLU layer as an activation function The ReLU layer is explained as an activation function that only activates a node if the input is above a certain quantity, demonstrating its role in the neural network.', 'Introduction to convolutional neural network layers and their functions The chapter introduces the three layers of a convolutional neural network - Convolutional layer, ReLU layer, pooling layer, and fully connected layer, and their respective functions in image classification.']}, {'end': 13408.972, 'start': 13132.143, 'title': 'Understanding relu and pooling layers', 'summary': 'Explains the relu function and its role in removing negative values from the output, resulting in 3 values after passing through the relu activation function, and then describes the process of shrinking the image through the pooling layer resulting in a 4x4 matrix, followed by the stacking of all layers.', 'duration': 276.829, 'highlights': ['The ReLU function removes negative values from the output, resulting in 3 values after passing through the ReLU activation function. After applying the ReLU function, the output is reduced to 3 values by removing negative values, which optimizes the output for further processing.', 'The pooling layer reduces the size of the image, resulting in a 4x4 matrix after moving the window throughout the image. By using a window size of 2x2 and taking the maximum value from each window, the pooling layer effectively shrinks the image from 7x7 to 4x4, optimizing the data for further processing.', 'Stacking up all the layers including convolution, ReLU, and pooling results in a further reduction of the image size to 2x2. After adding another layer of convolution, ReLU, and pooling, the image size is further reduced from 4x4 to 2x2, preparing the data for the fully connected layer.']}, {'end': 14022.036, 'start': 13409.312, 'title': 'Image classification and banknote authentication', 'summary': 'Discusses image classification using vectors and neural networks for x and o, and banknote authentication using features and labels with tensorflow, achieving an accuracy of 88%.', 'duration': 612.724, 'highlights': ['The chapter discusses the process of classifying images of X and O based on vector values, achieving a higher value of 0.91 for X compared to 0.51 for O, resulting in the correct classification of the input image as X. Classification of images of X and O based on vector values, achieving a higher value of 0.91 for X compared to 0.51 for O.', 'The use case involves training a model on different types of dog and cat images and achieving an accuracy of around 88% using TensorFlow, with steps including data set download, label encoding, image resizing, model building, and prediction. Training a model on dog and cat images, achieving an accuracy of around 88% using TensorFlow.', 'The chapter also delves into banknote authentication, utilizing wavelet transformed image variants, skewness, courtesies, and entropy as features, achieving an accuracy of 88% using TensorFlow for the classification of real and fake banknotes. Utilizing wavelet transformed image variants, skewness, courtesies, and entropy as features for banknote authentication, achieving an accuracy of 88% using TensorFlow.']}], 'duration': 1828.768, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM12193268.jpg', 'highlights': ['Using GPU leads to 132 times faster computation speed compared to CPU for object detection in TensorFlow.', 'Object detection in videos using OpenCV library and YOLO object detector is explained.', 'The process of comparing features, multiplying pixel values, and adding to obtain outputs in the convolution layer is explained.', 'The ReLU function removes negative values from the output, resulting in 3 values after passing through the ReLU activation function.', 'The chapter discusses the process of classifying images of X and O based on vector values, achieving a higher value of 0.91 for X compared to 0.51 for O.']}, {'end': 15558.367, 'segs': [{'end': 14101.637, 'src': 'embed', 'start': 14076.124, 'weight': 0, 'content': [{'end': 14082.693, 'text': "But what about those problems whose answer we have no clue of? So that's where our traditional approach was a failure.", 'start': 14076.124, 'duration': 6.569}, {'end': 14085.057, 'text': "So that's why neural networks were introduced.", 'start': 14083.194, 'duration': 1.863}, {'end': 14087.741, 'text': 'Now let us see what was the scenario after neural networks.', 'start': 14085.558, 'duration': 2.183}, {'end': 14092.671, 'text': 'So neural networks basically process information in a similar way the human brain does.', 'start': 14088.649, 'duration': 4.022}, {'end': 14095.913, 'text': 'And these networks, they actually learn from examples.', 'start': 14093.372, 'duration': 2.541}, {'end': 14098.695, 'text': 'You cannot program them to perform a specific task.', 'start': 14096.073, 'duration': 2.622}, {'end': 14101.637, 'text': 'They will learn from their examples, from their experience.', 'start': 14099.295, 'duration': 2.342}], 'summary': 'Neural networks learn from examples, processing info like human brain.', 'duration': 25.513, 'max_score': 14076.124, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM14076124.jpg'}, {'end': 14367.506, 'src': 'embed', 'start': 14337.91, 'weight': 4, 'content': [{'end': 14345.115, 'text': 'So when I talk about a neural network, it involves a lot of these artificial neurons with their own activation function and their processing element.', 'start': 14337.91, 'duration': 7.205}, {'end': 14352.101, 'text': "Now we'll move forward and we'll actually understand various modes of this perceptron or single artificial neuron.", 'start': 14346.577, 'duration': 5.524}, {'end': 14355.712, 'text': 'So there are two modes in a perceptron, one is training, another is using mode.', 'start': 14352.748, 'duration': 2.964}, {'end': 14360.637, 'text': 'In training mode, the neuron can be trained to fire for particular input patterns,', 'start': 14356.212, 'duration': 4.425}, {'end': 14367.506, 'text': "which means that we'll actually train our neuron to fire on certain set of inputs and to not fire on the other set of inputs.", 'start': 14360.637, 'duration': 6.869}], 'summary': 'Neural networks contain artificial neurons with activation functions, including training and using modes for pattern recognition.', 'duration': 29.596, 'max_score': 14337.91, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM14337910.jpg'}, {'end': 14952.166, 'src': 'embed', 'start': 14920.509, 'weight': 3, 'content': [{'end': 14924.413, 'text': 'So basically the most common algorithm for training a network is called back propagation.', 'start': 14920.509, 'duration': 3.904}, {'end': 14931.336, 'text': 'So what happens in back propagation, after the weighted sum of inputs and passing through an activation function and getting the output?', 'start': 14925.233, 'duration': 6.103}, {'end': 14934.117, 'text': 'we compare that output to the actual output that we already know.', 'start': 14931.336, 'duration': 2.781}, {'end': 14936.178, 'text': 'We figure out how much is the difference.', 'start': 14934.597, 'duration': 1.581}, {'end': 14937.559, 'text': 'We calculate the error.', 'start': 14936.498, 'duration': 1.061}, {'end': 14940.981, 'text': 'And based on that error, what we do, we propagate backwards.', 'start': 14937.979, 'duration': 3.002}, {'end': 14944.002, 'text': "And we'll see what happens when we change the weight.", 'start': 14941.521, 'duration': 2.481}, {'end': 14946.163, 'text': 'Will the error decrease or will it increase?', 'start': 14944.102, 'duration': 2.061}, {'end': 14952.166, 'text': 'And if it increases, when it increases by increasing the value of the variables or by decreasing the value of variables?', 'start': 14946.703, 'duration': 5.463}], 'summary': 'Back propagation algorithm adjusts weights based on error to decrease it.', 'duration': 31.657, 'max_score': 14920.509, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM14920509.jpg'}, {'end': 15519.415, 'src': 'embed', 'start': 15485.501, 'weight': 1, 'content': [{'end': 15490.524, 'text': 'After we have calculated the accuracy on the training data, we are going to plot it for every epoch how the accuracy is.', 'start': 15485.501, 'duration': 5.023}, {'end': 15495.627, 'text': 'And after plotting that, we are going to print the final accuracy which will be on our test data.', 'start': 15490.984, 'duration': 4.643}, {'end': 15498.588, 'text': "So using the same model, we'll make prediction on the test data.", 'start': 15495.987, 'duration': 2.601}, {'end': 15503.051, 'text': 'And after that, we are going to print the final accuracy and the mean squared error.', 'start': 15499.189, 'duration': 3.862}, {'end': 15505.412, 'text': "So let's go ahead and execute this, guys.", 'start': 15503.771, 'duration': 1.641}, {'end': 15509.287, 'text': 'All right, so training is done.', 'start': 15508.206, 'duration': 1.081}, {'end': 15512.289, 'text': 'And this is the graph we have got for accuracy versus epoch.', 'start': 15509.827, 'duration': 2.462}, {'end': 15515.832, 'text': 'This is accuracy, y-axis represents accuracy, whereas this is epochs.', 'start': 15512.689, 'duration': 3.143}, {'end': 15519.415, 'text': 'We have taken 100 epochs, and our accuracy has reached somewhere around 99%.', 'start': 15515.852, 'duration': 3.563}], 'summary': 'Trained model achieves 99% accuracy after 100 epochs.', 'duration': 33.914, 'max_score': 15485.501, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM15485501.jpg'}], 'start': 14022.996, 'title': 'Neural networks and training', 'summary': 'Discusses the importance of neural networks, understanding perceptron, neural network training, and implementation with tensorflow. it covers practical examples and achieves 99% accuracy after 100 epochs of training.', 'chapters': [{'end': 14297.404, 'start': 14022.996, 'title': 'Importance of neural networks', 'summary': 'Explains the importance of neural networks by comparing the limitations of conventional computers with the capabilities of neural networks, highlighting the self-learning capabilities, and demonstrating their importance through a practical example of identifying a dog in an image.', 'duration': 274.408, 'highlights': ['Neural networks process information similarly to the human brain, learning from examples and experiences, which allows them to solve problems even if the specific steps are unknown.', "Artificial neural networks simulate the human brain's analysis and processing of information, with self-learning capabilities that improve accuracy with more data.", 'Neural networks enable technologies such as language translation, virtual assistants, online grocery ordering, and chat bots, showcasing their impact on various applications and industries.', 'Neural networks enable machines to identify features and patterns, allowing them to recognize objects even without specific training on those objects, as demonstrated by identifying a dog in an image.', 'The limitations of conventional computers in solving problems without known specific steps led to the introduction of neural networks, which process information similarly to the human brain and learn from examples and experiences.']}, {'end': 14446.485, 'start': 14298.105, 'title': 'Understanding perceptron and neural networks', 'summary': 'Introduces the perceptron, its modes (training and using), various activation functions, and learning process, highlighting the importance of training before use and emphasizing the key role of activation functions in a neural network.', 'duration': 148.38, 'highlights': ['The chapter introduces the perceptron, its modes (training and using), various activation functions, and learning process. This encompasses the key topics covered in the chapter, providing an overview of the main concepts discussed.', 'Emphasizes the importance of training before use and the key role of activation functions in a neural network. It highlights the significance of training the neuron before using it and the crucial role of activation functions in the functioning of a neural network.', 'The perceptron has two modes: training and using mode. Describes the two operational modes of the perceptron, namely training mode for training the neuron to fire for specific input patterns and using mode for detecting taught input patterns.', 'The chapter explains three major activation functions: step function, sigmoid, and sine function. Details the three major activation functions used, providing an understanding of their characteristics and usage in artificial neurons.']}, {'end': 14901.368, 'start': 14447.432, 'title': 'Perceptron and neural networks', 'summary': 'Explains the concept of a perceptron using an analogy of deciding to attend a beer festival based on weather, company, and transport, demonstrating how weights and threshold values influence decision-making, and further delves into the structure and functioning of artificial neural networks with an example of image recognition.', 'duration': 453.936, 'highlights': ['The chapter explains the concept of a perceptron using an analogy of deciding to attend a beer festival based on weather, company, and transport, demonstrating how weights and threshold values influence decision-making. Analogy of deciding to attend a beer festival based on weather, company, and transport. Explanation of how weights and threshold values influence decision-making.', 'The chapter further delves into the structure and functioning of artificial neural networks with an example of image recognition. Explanation of the structure and functioning of artificial neural networks. Example of image recognition using neural networks.']}, {'end': 15169.483, 'start': 14901.928, 'title': 'Neural network training', 'summary': 'Explains the process of training a neural network using back propagation, involving error calculation, weight adjustment, and determination of weight increment or decrement based on square error, and provides an example of weight adjustment with input and output values.', 'duration': 267.555, 'highlights': ['Back propagation involves propagating error backwards and updating weights to minimize error through iterations. The back propagation process involves iterating through the weighted sum of inputs and activation function output, comparing the output with the actual output, calculating the error, propagating the error backwards, and updating the weights to minimize the error through multiple iterations.', 'Updating weights involves determining whether to increase or decrease the weight based on how the square error changes with the weight. The process of updating the weight involves determining whether to increase or decrease the weight based on the change in square error with the weight value, ultimately aiming to reach the minimum square error.', 'The example demonstrates the process of weight adjustment based on the desired output and model output, showcasing the consideration of increasing or decreasing the weight to minimize error. The provided example illustrates the process of adjusting the weight based on the desired output and model output, showcasing the consideration of increasing or decreasing the weight to minimize the error, ultimately determining the optimal weight value.']}, {'end': 15558.367, 'start': 15169.964, 'title': 'Neural network implementation with tensorflow', 'summary': 'Demonstrates the implementation of a neural network using tensorflow, achieving an accuracy of around 99% after training for 100 epochs, and also highlights the process of saving and restoring the model, with detailed information about the key steps and quantifiable data.', 'duration': 388.403, 'highlights': ['The model achieved an accuracy of around 99% after training for 100 epochs, showcasing the effectiveness of the implemented neural network in the use case.', 'The process of saving and restoring the model is explained in detail, covering the necessary steps and highlighting the files that appear once the model is saved.', 'The implementation includes the use of libraries such as Matplotlib for visualization, TensorFlow for neural networks, NumPy for arrays, Pandas for reading the data set, and sklearn for label encoding, shuffling, and splitting the data set into training and testing paths.', 'The chapter provides a detailed walkthrough of defining the neural network, including the initialization of important parameters like hidden layers and number of neurons, as well as the implementation of placeholders, weights, biases, and activation functions.', "The process of training the model involves calculating the error and accuracy for each epoch on the training data, plotting the accuracy for every epoch, and evaluating the final accuracy and mean squared error on the test data, showcasing the comprehensive assessment of the model's performance."]}], 'duration': 1535.371, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM14022996.jpg', 'highlights': ['Neural networks process information similarly to the human brain, learning from examples and experiences, which allows them to solve problems even if the specific steps are unknown.', 'The model achieved an accuracy of around 99% after training for 100 epochs, showcasing the effectiveness of the implemented neural network in the use case.', "The process of training the model involves calculating the error and accuracy for each epoch on the training data, plotting the accuracy for every epoch, and evaluating the final accuracy and mean squared error on the test data, showcasing the comprehensive assessment of the model's performance.", 'The back propagation process involves iterating through the weighted sum of inputs and activation function output, comparing the output with the actual output, calculating the error, propagating the error backwards, and updating the weights to minimize the error through multiple iterations.', 'The chapter introduces the perceptron, its modes (training and using), various activation functions, and learning process. This encompasses the key topics covered in the chapter, providing an overview of the main concepts discussed.']}, {'end': 16593.634, 'segs': [{'end': 15582.859, 'src': 'embed', 'start': 15558.367, 'weight': 0, 'content': [{'end': 15565.25, 'text': 'So all the values in the row of 754 and 768 will be fed to our model and our model will make prediction on that.', 'start': 15558.367, 'duration': 6.883}, {'end': 15567.071, 'text': 'So let us go ahead and run this.', 'start': 15565.751, 'duration': 1.32}, {'end': 15574.275, 'text': "So when I'm restoring my model, it seems that my model is hundred percent accurate for the values that I've fed it.", 'start': 15569.012, 'duration': 5.263}, {'end': 15582.859, 'text': "So, whatever values that I have actually given as input to my model, it has correctly identified its class, whether it's a fake note or a real note,", 'start': 15574.775, 'duration': 8.084}], 'summary': 'Model is 100% accurate in predicting fake or real notes for values 754 and 768.', 'duration': 24.492, 'max_score': 15558.367, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM15558367.jpg'}, {'end': 15663.244, 'src': 'embed', 'start': 15635.346, 'weight': 1, 'content': [{'end': 15638.008, 'text': 'First is modeling and diagnosing the cardiovascular system.', 'start': 15635.346, 'duration': 2.662}, {'end': 15642.751, 'text': 'So neural networks are used experimentally to model the human cardiovascular system.', 'start': 15638.748, 'duration': 4.003}, {'end': 15651.738, 'text': 'Diagnosis can be achieved by building a model of the cardiovascular system of an individual and comparing it with the real time physiological measurements taken from the patient.', 'start': 15643.533, 'duration': 8.205}, {'end': 15655.94, 'text': 'And trust me, guys, if this routine is carried out regularly,', 'start': 15652.378, 'duration': 3.562}, {'end': 15663.244, 'text': 'potential harmful medical conditions can be detected at an early stage and thus make the process of combating disease much easier.', 'start': 15655.94, 'duration': 7.304}], 'summary': 'Neural networks model cardiovascular system to diagnose and detect medical conditions early.', 'duration': 27.898, 'max_score': 15635.346, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM15635346.jpg'}, {'end': 15786.271, 'src': 'embed', 'start': 15756.223, 'weight': 2, 'content': [{'end': 15758.284, 'text': 'Now the second area is credit evaluation.', 'start': 15756.223, 'duration': 2.061}, {'end': 15759.564, 'text': "Now I'll give you an example here.", 'start': 15758.324, 'duration': 1.24}, {'end': 15763.185, 'text': 'The HNC company has developed several neural network applications,', 'start': 15759.604, 'duration': 3.581}, {'end': 15770.481, 'text': 'and one of them is a credit scoring system which increases the profitability of existing model up to 27%.', 'start': 15763.185, 'duration': 7.296}, {'end': 15772.783, 'text': "So these are few applications that I'm telling you guys.", 'start': 15770.481, 'duration': 2.302}, {'end': 15775.564, 'text': 'Neural network is actually the future.', 'start': 15773.243, 'duration': 2.321}, {'end': 15778.686, 'text': 'People are talking about neural networks everywhere.', 'start': 15776.165, 'duration': 2.521}, {'end': 15786.271, 'text': 'And especially after the introduction of GPUs and the amount of data that we have now, neural network is actually spreading like plague right now.', 'start': 15779.087, 'duration': 7.184}], 'summary': "Hnc's credit scoring system boosts profitability by 27% using neural networks.", 'duration': 30.048, 'max_score': 15756.223, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM15756223.jpg'}, {'end': 16530.776, 'src': 'embed', 'start': 16501.736, 'weight': 3, 'content': [{'end': 16504.636, 'text': 'Now for vanishing gradient, we can use a ReLU activation function.', 'start': 16501.736, 'duration': 2.9}, {'end': 16507.578, 'text': 'Similarly, we can also use LSTM and GRUs.', 'start': 16504.917, 'duration': 2.661}, {'end': 16510.598, 'text': 'Now let us understand what exactly are LSTMs.', 'start': 16508.218, 'duration': 2.38}, {'end': 16515.085, 'text': 'So guys, we saw what are the two limitations with the recurrent neural networks.', 'start': 16511.482, 'duration': 3.603}, {'end': 16518.307, 'text': "Now we'll understand how we can solve that with the help of LSTMs.", 'start': 16515.525, 'duration': 2.782}, {'end': 16526.293, 'text': 'Now what are LSTMs? Long short term memory networks, usually called as LSTMs, are nothing but a special kind of recurrent neural network.', 'start': 16518.748, 'duration': 7.545}, {'end': 16530.776, 'text': 'And these recurrent neural networks are capable of learning long term dependencies.', 'start': 16526.714, 'duration': 4.062}], 'summary': 'Using lstms can solve vanishing gradient problem in rnns, capable of learning long term dependencies.', 'duration': 29.04, 'max_score': 16501.736, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM16501736.jpg'}], 'start': 15558.367, 'title': 'Model accuracy prediction and applications of neural networks', 'summary': "Discusses a model's 100% accuracy in predicting fake and real notes using input data of 754 and 768, as well as the extensive applications of neural networks in medicine and business, addressing early disease detection, profitability increase, and challenges related to recurrent neural networks and backpropagation.", 'chapters': [{'end': 15593.415, 'start': 15558.367, 'title': 'Model accuracy prediction', 'summary': "Discusses the model's hundred percent accuracy in predicting fake and real notes based on the input data of 754 and 768, with zero standing for fake notes and one for real notes, using the original class and predicted class.", 'duration': 35.048, 'highlights': ['Model achieved hundred percent accuracy in predicting fake and real notes based on input data of 754 and 768, with zero standing for fake notes and one for real notes.', "The model correctly identified the class of the input data as fake or real, with the original class being zero and the model's prediction also being zero."]}, {'end': 16593.634, 'start': 15594.015, 'title': 'Applications of neural networks', 'summary': 'Discusses the extensive applications of neural networks in medicine and business, including the potential impact on early disease detection and profitability increase, while also exploring the challenges and solutions related to recurrent neural networks and backpropagation.', 'duration': 999.619, 'highlights': ['Neural networks in medicine Artificial neural networks are a hot research area, with extensive applications expected in the next few years to biomedical systems, particularly in modeling the cardiovascular system and recognizing diseases from various scans.', "Business applications of neural networks Neural networks have potential applications in marketing, such as the airline marketing tactician, and credit evaluation, with examples like the HNC company's credit scoring system increasing profitability by up to 27%.", 'Challenges with recurrent neural networks The chapter delves into the issues of vanishing and exploding gradients during backpropagation, highlighting the impact on long-term dependencies and solutions such as truncated BTT, gradient clipping, and the use of ReLU activation function, LSTM, and GRUs.']}], 'duration': 1035.267, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM15558367.jpg', 'highlights': ['Model achieved 100% accuracy in predicting fake and real notes based on input data of 754 and 768.', 'Neural networks have extensive applications in medicine, particularly in modeling the cardiovascular system and recognizing diseases from various scans.', "Neural networks also have potential applications in business, such as in marketing and credit evaluation, with examples like the HNC company's credit scoring system increasing profitability by up to 27%.", 'Challenges with recurrent neural networks include issues of vanishing and exploding gradients during backpropagation, impacting long-term dependencies and solutions such as truncated BTT, gradient clipping, and the use of ReLU activation function, LSTM, and GRUs.']}, {'end': 18219.725, 'segs': [{'end': 16621.615, 'src': 'embed', 'start': 16594.235, 'weight': 0, 'content': [{'end': 16600.182, 'text': "And it's entirely possible for the gap between the relevant information and the point where it is needed to become very large.", 'start': 16594.235, 'duration': 5.947}, {'end': 16602.583, 'text': 'And this is nothing but long-term dependencies.', 'start': 16600.642, 'duration': 1.941}, {'end': 16606.629, 'text': 'And the LSTMs are capable of handling such long-term dependencies.', 'start': 16603.064, 'duration': 3.565}, {'end': 16611.174, 'text': 'Now LSTMs also have a chain-like structure, like recurrent neural networks.', 'start': 16607.349, 'duration': 3.825}, {'end': 16615.892, 'text': 'Now all the recurrent neural networks have the form of a chain of repeating modules of neural networks.', 'start': 16611.65, 'duration': 4.242}, {'end': 16621.615, 'text': 'Now in standard RNNs, the repeating module will have a very simple structure such as a single tanh layer that you can see.', 'start': 16616.312, 'duration': 5.303}], 'summary': 'Lstms handle long-term dependencies in neural networks efficiently.', 'duration': 27.38, 'max_score': 16594.235, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM16594235.jpg'}, {'end': 16659.233, 'src': 'embed', 'start': 16630.24, 'weight': 1, 'content': [{'end': 16631.78, 'text': "Alright, that's why we use tanh.", 'start': 16630.24, 'duration': 1.54}, {'end': 16633.982, 'text': 'And this is an example of an RNN.', 'start': 16632.201, 'duration': 1.781}, {'end': 16636.924, 'text': "Now we'll understand what exactly are LSTMs.", 'start': 16634.342, 'duration': 2.582}, {'end': 16639.085, 'text': 'Now this is a structure of an LSTM.', 'start': 16637.444, 'duration': 1.641}, {'end': 16645.226, 'text': 'If you notice, LSTM also have a chain-like structure, but the repeating module has different structures.', 'start': 16639.805, 'duration': 5.421}, {'end': 16649.868, 'text': 'Instead of having a single neural network layer, there are four interacting in a very special way.', 'start': 16645.788, 'duration': 4.08}, {'end': 16652.33, 'text': 'Now the key to LSTM is the cell state.', 'start': 16650.309, 'duration': 2.021}, {'end': 16656.692, 'text': "Now this particular line that I'm highlighting, this is what is called the cell state.", 'start': 16652.669, 'duration': 4.023}, {'end': 16659.233, 'text': 'The horizontal line running through the top of the diagram.', 'start': 16657.052, 'duration': 2.181}], 'summary': 'Introduction to lstms in rnn, featuring 4 interacting layers and the key cell state.', 'duration': 28.993, 'max_score': 16630.24, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM16630240.jpg'}, {'end': 16717.685, 'src': 'embed', 'start': 16688.169, 'weight': 2, 'content': [{'end': 16690.772, 'text': "So the layer that I'm highlighting with my cursor, it is a sigmoid layer.", 'start': 16688.169, 'duration': 2.603}, {'end': 16692.572, 'text': 'called the forget gate layer.', 'start': 16691.412, 'duration': 1.16}, {'end': 16700.415, 'text': 'It looks at ht, minus one, that is the information from the previous timestamp, and xt, which is the new input and outputs,', 'start': 16693.053, 'duration': 7.362}, {'end': 16704.036, 'text': 'a number between zeros and one for each number in the cell state.', 'start': 16700.415, 'duration': 3.621}, {'end': 16706.497, 'text': 'ct minus one, which is coming from the previous timestamp.', 'start': 16704.036, 'duration': 2.461}, {'end': 16711.338, 'text': 'One represents completely keep this, while a zero represents completely get rid of this.', 'start': 16706.997, 'duration': 4.341}, {'end': 16717.685, 'text': 'Now if we go back to our example of a language model trying to predict the next word based on all the previous ones.', 'start': 16711.96, 'duration': 5.725}], 'summary': 'Sigmoid layer in lstm model outputs values between 0 and 1 to retain or discard information from previous timestamp.', 'duration': 29.516, 'max_score': 16688.169, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM16688169.jpg'}, {'end': 16807.691, 'src': 'embed', 'start': 16783.679, 'weight': 3, 'content': [{'end': 16790.602, 'text': "First, a sigmoid layer, this is called a sigmoid layer, which is also known as an input gate layer, decide which values we'll update.", 'start': 16783.679, 'duration': 6.923}, {'end': 16792.563, 'text': 'All right, so what values we need to update.', 'start': 16791.022, 'duration': 1.541}, {'end': 16800.906, 'text': "Then there's also a tanhash layer that creates a vector of the candidate values, c bar of t minus one, that will be added to the state later on.", 'start': 16792.903, 'duration': 8.003}, {'end': 16803.548, 'text': 'All right, so let me explain it to you in a simpler terms.', 'start': 16801.386, 'duration': 2.162}, {'end': 16807.691, 'text': 'So, whatever input that we are getting from the previous timestamp and the new input,', 'start': 16803.828, 'duration': 3.863}], 'summary': 'Neural network layers (sigmoid and tanh) process and update input values.', 'duration': 24.012, 'max_score': 16783.679, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM16783679.jpg'}, {'end': 17147.294, 'src': 'embed', 'start': 17123.42, 'weight': 4, 'content': [{'end': 17129.562, 'text': "We'll be using TensorFlow which is a popular Python library for implementing deep neural networks or neural networks in general.", 'start': 17123.42, 'duration': 6.142}, {'end': 17131.863, 'text': "Alright so I'll quickly open my PyCharm now.", 'start': 17130.182, 'duration': 1.681}, {'end': 17136.984, 'text': "So guys this is my PyCharm and over here I've already written the code in order to execute the use case that we have.", 'start': 17132.483, 'duration': 4.501}, {'end': 17141.732, 'text': 'So first we need to do is import libraries NumPy Ferraris, TensorFlow.', 'start': 17137.404, 'duration': 4.328}, {'end': 17147.294, 'text': 'we know TensorFlow.contra from that we need to import RNN in random collections in time.', 'start': 17141.732, 'duration': 5.562}], 'summary': 'Using tensorflow in pycharm to implement neural networks.', 'duration': 23.874, 'max_score': 17123.42, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM17123420.jpg'}, {'end': 17263.31, 'src': 'embed', 'start': 17233.39, 'weight': 5, 'content': [{'end': 17236.593, 'text': 'We are feeding in our story and calling the function read underscore data.', 'start': 17233.39, 'duration': 3.203}, {'end': 17238.715, 'text': 'Then what we are doing, we are creating a dictionary.', 'start': 17236.893, 'duration': 1.822}, {'end': 17243.38, 'text': 'What is a dictionary? We all know key value pairs based on the frequency of occurrences of each symbol.', 'start': 17238.735, 'duration': 4.645}, {'end': 17247.462, 'text': 'Alright, so from here collections.counter words.mostcommon.', 'start': 17243.86, 'duration': 3.602}, {'end': 17251.084, 'text': 'So most common words with their frequency of occurrence, there will be a dictionary created.', 'start': 17247.482, 'duration': 3.602}, {'end': 17254.085, 'text': "And after that, we'll call this dict function.", 'start': 17251.764, 'duration': 2.321}, {'end': 17259.048, 'text': 'And this dict function will feed in word, and which is equal to length of dictionary.', 'start': 17254.566, 'duration': 4.482}, {'end': 17263.31, 'text': 'That means whatever the length of that particular dictionary is, how many times it is repeated.', 'start': 17259.088, 'duration': 4.222}], 'summary': 'Using the read_data function, a dictionary is created based on symbol occurrence frequency, and a dict function is called to calculate the length of the dictionary.', 'duration': 29.92, 'max_score': 17233.39, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM17233390.jpg'}, {'end': 17326.921, 'src': 'embed', 'start': 17301.115, 'weight': 6, 'content': [{'end': 17307.458, 'text': "So batch size is what? After every thousand epochs, you'll see the output, all right? So it'll be processing it in batches of thousand iterations.", 'start': 17301.115, 'duration': 6.343}, {'end': 17309.54, 'text': 'Then we have N underscore input S3.', 'start': 17307.959, 'duration': 1.581}, {'end': 17314.403, 'text': 'Now the number of units in the RNN cell will keep it as 512.', 'start': 17309.92, 'duration': 4.483}, {'end': 17316.324, 'text': 'Then we need to define X and Y.', 'start': 17314.403, 'duration': 1.921}, {'end': 17323.108, 'text': 'So X will be our placeholder that will have the input values, and Y will have all the labels, all right, over cap size.', 'start': 17316.324, 'duration': 6.784}, {'end': 17326.921, 'text': "So X is a placeholder where we'll be feeding in our input dictionary.", 'start': 17323.959, 'duration': 2.962}], 'summary': 'Model uses batch size of 1000, 512 rnn units, x and y placeholders for input and labels.', 'duration': 25.806, 'max_score': 17301.115, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM17301115.jpg'}, {'end': 17407.138, 'src': 'embed', 'start': 17380.221, 'weight': 7, 'content': [{'end': 17387.504, 'text': 'we are using reduce underscore mean softmax cross entropy, and this will give us basically the probability of each symbol.', 'start': 17380.221, 'duration': 7.283}, {'end': 17390.926, 'text': 'and then we are optimizing it using RMS prop optimizer.', 'start': 17387.504, 'duration': 3.422}, {'end': 17396.088, 'text': "All right, and this gives actually a better accuracy than Adam optimizer, and that's the reason why we are using it.", 'start': 17391.486, 'duration': 4.602}, {'end': 17398.35, 'text': 'Then we are going to calculate the accuracy.', 'start': 17396.468, 'duration': 1.882}, {'end': 17401.933, 'text': 'And after that, we are going to initialize the variables that we have used.', 'start': 17398.91, 'duration': 3.023}, {'end': 17407.138, 'text': 'As we have seen in TensorFlow, that we need to initialize all the variables, unlike constants and placeholders in TensorFlow.', 'start': 17402.033, 'duration': 5.105}], 'summary': 'Using rms prop optimizer for better accuracy, then calculating and initializing variables in tensorflow.', 'duration': 26.917, 'max_score': 17380.221, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM17380221.jpg'}, {'end': 17493.162, 'src': 'embed', 'start': 17468.564, 'weight': 8, 'content': [{'end': 17474.37, 'text': 'And this council will be fed back as a part of the new input, and our new input will be a general council.', 'start': 17468.564, 'duration': 5.806}, {'end': 17475.991, 'text': 'So it will be a general council.', 'start': 17474.41, 'duration': 1.581}, {'end': 17480.136, 'text': 'Alright, so these three words will become our new input to predict the new output, which is two.', 'start': 17476.312, 'duration': 3.824}, {'end': 17481.277, 'text': 'Alright, and so on.', 'start': 17480.536, 'duration': 0.741}, {'end': 17486.198, 'text': 'So surprisingly, LSTM actually creates a story that, you know, somehow makes sense.', 'start': 17481.755, 'duration': 4.443}, {'end': 17487.098, 'text': "So let's just read it.", 'start': 17486.278, 'duration': 0.82}, {'end': 17493.162, 'text': 'Had a general counselor to consider what measures they could take to outwit their common enemy, the cat.', 'start': 17487.598, 'duration': 5.564}], 'summary': "Using lstm, the new input 'general council' predicts the new output as two, creating a coherent story.", 'duration': 24.598, 'max_score': 17468.564, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM17468564.jpg'}, {'end': 17601.997, 'src': 'embed', 'start': 17581.445, 'weight': 9, 'content': [{'end': 17591.651, 'text': 'Well guys Kiras had over 4800 contributors during its launch and the initial stages and now that number has gone up to 250,000 active developers.', 'start': 17581.445, 'duration': 10.206}, {'end': 17597.735, 'text': 'Well, what amuses me is that there is a 2x growth ever since, every year since its launch.', 'start': 17592.071, 'duration': 5.664}, {'end': 17601.997, 'text': 'also, it holds a really good amount of traction among multiple startups.', 'start': 17597.735, 'duration': 4.262}], 'summary': 'Kiras had 4800 contributors at launch, now 250,000 active developers, with 2x annual growth and strong startup traction.', 'duration': 20.552, 'max_score': 17581.445, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM17581445.jpg'}, {'end': 17663.802, 'src': 'embed', 'start': 17638.929, 'weight': 10, 'content': [{'end': 17645.295, 'text': 'Well guys the focus on user experience has always been the major part of Keras and next large adoption in the industry.', 'start': 17638.929, 'duration': 6.366}, {'end': 17649.539, 'text': 'Definitely. We just checked out all of the industry traction it gets, and this holds well.', 'start': 17645.475, 'duration': 4.064}, {'end': 17652.702, 'text': 'and next it is multi backend and supports multi-platform as well.', 'start': 17649.539, 'duration': 3.163}, {'end': 17655.645, 'text': 'This helps all the coders come together and code easily.', 'start': 17653.082, 'duration': 2.563}, {'end': 17661.018, 'text': 'Next up the research community present for Kiras is amazing along with the production community.', 'start': 17656.431, 'duration': 4.587}, {'end': 17662.54, 'text': 'So this is a win-win for me guys.', 'start': 17661.058, 'duration': 1.482}, {'end': 17663.802, 'text': 'So what do you think?', 'start': 17662.58, 'duration': 1.222}], 'summary': 'Keras prioritizes user experience, with strong industry adoption, multi-backend support, and community presence.', 'duration': 24.873, 'max_score': 17638.929, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM17638929.jpg'}, {'end': 17979.957, 'src': 'embed', 'start': 17951.397, 'weight': 11, 'content': [{'end': 17953.017, 'text': "It's pretty much the same here as well.", 'start': 17951.397, 'duration': 1.62}, {'end': 17959.859, 'text': 'Well, the highlights of the functional model is that it supports multi input multi output and an arbitrary static graph topology.', 'start': 17953.398, 'duration': 6.461}, {'end': 17961.019, 'text': 'We have branches.', 'start': 17960.259, 'duration': 0.76}, {'end': 17966.6, 'text': 'So whenever we have a complex model, the model is forward into two or more branches based on the requirement guys.', 'start': 17961.319, 'duration': 5.281}, {'end': 17972.835, 'text': 'The code which we have here is pretty much similar to the previous one, but with subtle changes we first import the models.', 'start': 17967.121, 'duration': 5.714}, {'end': 17975.904, 'text': 'We work on its architecture and lastly we train the network.', 'start': 17972.976, 'duration': 2.928}, {'end': 17977.856, 'text': 'Well with functional models.', 'start': 17976.696, 'duration': 1.16}, {'end': 17979.957, 'text': 'We have this concept called as domain adoption.', 'start': 17977.896, 'duration': 2.061}], 'summary': 'Functional model supports multi input multi output and arbitrary static graph topology, with domain adoption concept.', 'duration': 28.56, 'max_score': 17951.397, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM17951397.jpg'}, {'end': 18029.117, 'src': 'embed', 'start': 18004.349, 'weight': 12, 'content': [{'end': 18010.271, 'text': 'So moving on we need to understand about the two basic types of execution in Keras deferred and eager execution.', 'start': 18004.349, 'duration': 5.922}, {'end': 18013.552, 'text': 'It is also called a symbolic and imperative execution as well.', 'start': 18010.591, 'duration': 2.961}, {'end': 18014.832, 'text': 'Well with deferred.', 'start': 18013.932, 'duration': 0.9}, {'end': 18022.995, 'text': 'we use Python to build a computation graph first, like we previously discussed, and then this compiled graph gets executed well with eager execution.', 'start': 18014.832, 'duration': 8.163}, {'end': 18024.355, 'text': 'There is a slight change guys.', 'start': 18023.055, 'duration': 1.3}, {'end': 18029.117, 'text': 'It is here that the Python runtime itself becomes the execution runtime for all of the models.', 'start': 18024.696, 'duration': 4.421}], 'summary': 'Keras has two types of execution: deferred (symbolic) and eager (imperative). python runtime is used for eager execution.', 'duration': 24.768, 'max_score': 18004.349, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM18004349.jpg'}, {'end': 18097.384, 'src': 'embed', 'start': 18064.856, 'weight': 13, 'content': [{'end': 18067.498, 'text': 'So starting out we need to prepare the inputs for the model.', 'start': 18064.856, 'duration': 2.642}, {'end': 18071.802, 'text': 'We do this by analyzing our requirements and specifying the input dimensions.', 'start': 18067.899, 'duration': 3.903}, {'end': 18077.968, 'text': 'Well, as you know what the common inputs are images videos text or audio based on your model requirement.', 'start': 18072.263, 'duration': 5.705}, {'end': 18082.451, 'text': 'The next step is to actually define the artificial neural network model here.', 'start': 18078.488, 'duration': 3.963}, {'end': 18089.557, 'text': 'We do everything from defining the model architecture to building the computation graph and also defining the style will be using for the model.', 'start': 18082.511, 'duration': 7.046}, {'end': 18091.119, 'text': 'It is as straightforward as that.', 'start': 18089.697, 'duration': 1.422}, {'end': 18097.384, 'text': 'Well step 3 is to specify the optimizer think of it this way a neural network is just a complex function.', 'start': 18091.659, 'duration': 5.725}], 'summary': 'Prepare inputs, define model, specify optimizer for neural network.', 'duration': 32.528, 'max_score': 18064.856, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM18064856.jpg'}, {'end': 18197.066, 'src': 'embed', 'start': 18170.606, 'weight': 14, 'content': [{'end': 18175.41, 'text': "So we're trying to predict the price of a bottle of wine just by knowing the description and the variety of wine.", 'start': 18170.606, 'duration': 4.804}, {'end': 18179.393, 'text': 'Well, we can work this out with the Keras functional API and tensorflow.', 'start': 18175.97, 'duration': 3.423}, {'end': 18184.036, 'text': "We'll be building a wide and a deep network using Keras to make predictions for us.", 'start': 18179.773, 'duration': 4.263}, {'end': 18187.158, 'text': 'Well, can we achieve this goal? Yes, we can.', 'start': 18184.677, 'duration': 2.481}, {'end': 18191.582, 'text': 'This is a problem statement suited for wide and deep learning networks as I mentioned.', 'start': 18187.639, 'duration': 3.943}, {'end': 18197.066, 'text': "Well, it involves textual input and there isn't any correlation between the wines description and its price.", 'start': 18191.982, 'duration': 5.084}], 'summary': 'Using keras and tensorflow, we aim to predict wine prices from descriptions and varieties, with a suitable wide and deep learning network.', 'duration': 26.46, 'max_score': 18170.606, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM18170606.jpg'}], 'start': 16594.235, 'title': 'Understanding lstms and rnn model training', 'summary': "Details long short-term memory (lstm) networks and their capabilities, delves into lstm implementation in python using tensorflow, and discusses the rnn model training process, encompassing data preprocessing, model architecture, and lstm's ability to create coherent stories. additionally, it introduces keras as a high-level api of tensorflow and explores its features, use cases, and implementation steps.", 'chapters': [{'end': 16783.299, 'start': 16594.235, 'title': 'Understanding lstms in rnns', 'summary': 'Explains the concept of long short-term memory (lstm) networks, detailing their capability to handle long-term dependencies and the structure of lstm cells, including the functions of the forget gate layer and the process of deciding what information to store in the cell state.', 'duration': 189.064, 'highlights': ['LSTMs are capable of handling long-term dependencies LSTMs are effective in managing long-term dependencies, which can lead to a significant gap between relevant information and its point of use.', 'Structure of an LSTM LSTMs have a chain-like structure with repeating modules of neural networks, where the key component is the cell state, which acts as a conveyor belt running through the entire chain with minor linear interactions.', 'Functions of the forget gate layer The forget gate layer, represented by a sigmoid layer, decides what information to discard from the cell state based on the previous timestamp and new input, using an output between zero and one for each element in the cell state.']}, {'end': 17233.11, 'start': 16783.679, 'title': 'Understanding lstm and implementing it in python', 'summary': 'Explains the concept of lstm layers and their functionalities, providing a detailed breakdown of the lstm process and its use in predicting the next word in a sentence. it then delves into the implementation of lstm using tensorflow in python, including the necessary libraries, file reading, data processing, and model training.', 'duration': 449.431, 'highlights': ['The chapter explains the concept of LSTM layers and their functionalities It provides a detailed breakdown of the LSTM process, including the sigmoid and tanh layers, and their roles in updating and combining values.', 'Delving into the implementation of LSTM using TensorFlow in Python It includes the necessary libraries, file reading, data processing, and model training, with a focus on converting unique symbols into integer values and creating a dictionary for training.']}, {'end': 17519.734, 'start': 17233.39, 'title': 'Rnn model training process', 'summary': "Discusses the process of training a model using rnn, starting from data preprocessing, defining parameters, model architecture, loss optimization, accuracy calculation, handling exceptions, and generating predictions, with a focus on lstm's ability to create coherent stories.", 'duration': 286.344, 'highlights': ['Data Preprocessing and Dictionary Creation The chapter explains the process of creating a dictionary based on the frequency of symbol occurrences and feeding it into the build_data_set function, ultimately defining the vocabulary size and most common words with their frequency.', 'Model Architecture and Parameters The discussion covers the definition of various parameters such as learning rate, training iterations, display step, batch size, and RNN cell units, along with placeholders for input and labels, weights, biases, and the model architecture using a two-layer LSTM.', "Loss Optimization and Accuracy Calculation The chapter details the process of calculating loss using softmax cross entropy, optimizing it with RMS prop, and calculating the model's accuracy, along with the initialization of variables and handling exceptions for out-of-dictionary words.", "Prediction Generation and LSTM Story Creation The discussion highlights the generation of predictions, particularly focusing on LSTM's ability to create coherent stories based on predicted outputs fed back as inputs, exemplifying a story created by the model based on input words and their predicted outputs."]}, {'end': 18219.725, 'start': 17525.683, 'title': 'Keras: python-based deep learning framework', 'summary': 'Introduces keras as a high-level api of tensorflow, highlighting its simplicity, industry traction, contributors, and major features. it explores the user experience, multi-platform support, research and production community, ease of use, fast processing, and flexibility, as well as the working principle, computational graph, and two major models. it also discusses deferred and eager execution, and the five steps to implement a neural network using keras. additionally, it presents a use case of building a wide and deep network for predicting wine prices using the keras functional api and tensorflow.', 'duration': 694.042, 'highlights': ['Keras Industry Traction and Contributors Keras has over 250,000 active developers and 2x growth annually since its launch, with major contributions from big players like Microsoft, Google, Nvidia, and Amazon, and industry usage in firms like Netflix, Uber, and Expedia.', 'User Experience and Multi-Platform Support Keras emphasizes user experience, supports multi-backend and multi-platform, and offers easy comprehension of concepts, fast processing, seamless CPU/GPU integration, and freedom to design on any architecture.', "Functional Model and Sequential Model in Keras The chapter presents the functional model's support for multi-input/output and static graph topology, while the sequential model is highlighted for its simplicity, linear stack of layers, and use in building simple classification networks and encoder-decoder models.", 'Deferred and Eager Execution in Keras Keras supports both deferred (symbolic) and eager (imperative) execution, with deferred execution involving building a computation graph first and eager execution utilizing Python runtime, similar to numpy, and supporting value-dependent dynamic topology structures.', 'Steps to Implement a Neural Network Using Keras The chapter outlines the five major steps to implement a neural network using Keras, including preparing inputs, defining the artificial neural network model, specifying the optimizer, defining the loss function, and training/testing the network.', 'Use Case: Wine Classifier with Keras Functional API and TensorFlow The use case involves predicting the price of wine using the Keras functional API and TensorFlow, suited for wide and deep learning networks, and emphasizing textual input and the absence of correlation between wine description and price.']}], 'duration': 1625.49, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM16594235.jpg', 'highlights': ['LSTMs are capable of handling long-term dependencies, which can lead to a significant gap between relevant information and its point of use.', 'LSTMs have a chain-like structure with repeating modules of neural networks, where the key component is the cell state, which acts as a conveyor belt running through the entire chain with minor linear interactions.', 'The forget gate layer, represented by a sigmoid layer, decides what information to discard from the cell state based on the previous timestamp and new input, using an output between zero and one for each element in the cell state.', 'The chapter explains the concept of LSTM layers and their functionalities, including the sigmoid and tanh layers, and their roles in updating and combining values.', 'Delving into the implementation of LSTM using TensorFlow in Python, including the necessary libraries, file reading, data processing, and model training, with a focus on converting unique symbols into integer values and creating a dictionary for training.', 'Data Preprocessing and Dictionary Creation, defining the vocabulary size and most common words with their frequency.', 'Model Architecture and Parameters, covering various parameters such as learning rate, training iterations, display step, batch size, and RNN cell units, along with placeholders for input and labels, weights, biases, and the model architecture using a two-layer LSTM.', "Loss Optimization and Accuracy Calculation, including the process of calculating loss using softmax cross entropy, optimizing it with RMS prop, and calculating the model's accuracy, along with the initialization of variables and handling exceptions for out-of-dictionary words.", "Prediction Generation and LSTM Story Creation, highlighting the generation of predictions and LSTM's ability to create coherent stories based on predicted outputs fed back as inputs.", 'Keras has over 250,000 active developers and 2x growth annually since its launch, with major contributions from big players like Microsoft, Google, Nvidia, and Amazon, and industry usage in firms like Netflix, Uber, and Expedia.', 'Keras emphasizes user experience, supports multi-backend and multi-platform, and offers easy comprehension of concepts, fast processing, seamless CPU/GPU integration, and freedom to design on any architecture.', "The chapter presents the functional model's support for multi-input/output and static graph topology, while the sequential model is highlighted for its simplicity, linear stack of layers, and use in building simple classification networks and encoder-decoder models.", 'Keras supports both deferred (symbolic) and eager (imperative) execution, with deferred execution involving building a computation graph first and eager execution utilizing Python runtime, similar to numpy, and supporting value-dependent dynamic topology structures.', 'The chapter outlines the five major steps to implement a neural network using Keras, including preparing inputs, defining the artificial neural network model, specifying the optimizer, defining the loss function, and training/testing the network.', 'The use case involves predicting the price of wine using the Keras functional API and TensorFlow, suited for wide and deep learning networks, and emphasizing textual input and the absence of correlation between wine description and price.']}, {'end': 19028.77, 'segs': [{'end': 18245.34, 'src': 'embed', 'start': 18220.125, 'weight': 6, 'content': [{'end': 18225.269, 'text': 'However, the functional model allows for more flexibility and is best suited for models with multiple inputs.', 'start': 18220.125, 'duration': 5.144}, {'end': 18228.431, 'text': 'So we need to know a little bit about wide and deep model guys.', 'start': 18225.689, 'duration': 2.742}, {'end': 18231.593, 'text': 'Well wide models are models with sparse feature vectors.', 'start': 18228.811, 'duration': 2.782}, {'end': 18242.239, 'text': 'Well, what I mean by sparse feature vectors is that it consists of mostly zeros and a little bit of ones and deep networks and networks which do really well on tasks like speech and image recognition.', 'start': 18231.973, 'duration': 10.266}, {'end': 18245.34, 'text': 'So now that that sorted we need to take a look at the data set.', 'start': 18242.619, 'duration': 2.721}], 'summary': 'Functional model is best for models with multiple inputs. wide models have sparse feature vectors, while deep networks excel in tasks like speech and image recognition.', 'duration': 25.215, 'max_score': 18220.125, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM18220125.jpg'}, {'end': 18277.183, 'src': 'embed', 'start': 18248.782, 'weight': 0, 'content': [{'end': 18253.065, 'text': "So what's the data? Well, it's basically 12 columns of data and it's as follows.", 'start': 18248.782, 'duration': 4.283}, {'end': 18256.188, 'text': "Here we'll be talking about the country that the wine is from.", 'start': 18253.485, 'duration': 2.703}, {'end': 18257.569, 'text': 'next up is description.', 'start': 18256.188, 'duration': 1.381}, {'end': 18262.334, 'text': 'a few sentences from the sommelier Descripting the wines taste, smell, look and feel.', 'start': 18257.569, 'duration': 4.765}, {'end': 18265.237, 'text': 'a sommelier is a person who is a professional wine taster, guys.', 'start': 18262.334, 'duration': 2.903}, {'end': 18270.443, 'text': 'Next up is designation the vineyard within the winery where the grapes at the wine has been made from.', 'start': 18265.778, 'duration': 4.665}, {'end': 18277.183, 'text': 'Next up is points the number of points that the wine enthusiasts rated the wine on a scale of 1 to 10..', 'start': 18271.377, 'duration': 5.806}], 'summary': 'Transcript describes 12 columns of wine data including country, description, sommelier details, designation, and points on a scale of 1 to 10.', 'duration': 28.401, 'max_score': 18248.782, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM18248782.jpg'}, {'end': 18366.395, 'src': 'embed', 'start': 18332.233, 'weight': 1, 'content': [{'end': 18340.196, 'text': 'overall goal here is to actually create a model that can identify the variety, winery and the location of a wine based on the description alone,', 'start': 18332.233, 'duration': 7.963}, {'end': 18346.398, 'text': 'and this data set offers some really great opportunities for sentiment analysis and other text related predictive models as well.', 'start': 18340.196, 'duration': 6.202}, {'end': 18348.038, 'text': "So now that that's clear.", 'start': 18346.918, 'duration': 1.12}, {'end': 18349.959, 'text': 'We need to take a look at the sample data case.', 'start': 18348.098, 'duration': 1.861}, {'end': 18357.008, 'text': "So here we have a description for the wine such as scent if it's tart firm or needs more decanting Etc.", 'start': 18350.783, 'duration': 6.225}, {'end': 18361.491, 'text': 'So this forms our input for the model guys, and the output our model provides.', 'start': 18357.388, 'duration': 4.103}, {'end': 18364.834, 'text': 'just from all of this textual information is the pricing that it predicts.', 'start': 18361.491, 'duration': 3.343}, {'end': 18366.395, 'text': 'How cool is that, guys?', 'start': 18365.234, 'duration': 1.161}], 'summary': 'Create model to identify wine variety, winery, and location based on description. dataset also offers opportunities for sentiment analysis and predictive models.', 'duration': 34.162, 'max_score': 18332.233, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM18332233.jpg'}, {'end': 18961.492, 'src': 'embed', 'start': 18929.142, 'weight': 4, 'content': [{'end': 18932.303, 'text': "So guys here's the important thing that you have to notice with every Epoch.", 'start': 18929.142, 'duration': 3.161}, {'end': 18942.486, 'text': 'We were actually reducing the loss all the way from 1100 to 130 guys, and the accuracy of prediction went from 0.02 all the way to 0.0994,', 'start': 18932.323, 'duration': 10.163}, {'end': 18944.287, 'text': 'which is almost 0.1..', 'start': 18942.486, 'duration': 1.801}, {'end': 18947.448, 'text': "Well, wow, that's definitely a breakthrough for just 10 passes guys.", 'start': 18944.287, 'duration': 3.161}, {'end': 18949.761, 'text': 'And now that the training is done.', 'start': 18948.34, 'duration': 1.421}, {'end': 18951.063, 'text': "It's time to evaluate it.", 'start': 18949.902, 'duration': 1.161}, {'end': 18953.265, 'text': 'So let me go ahead and run this piece of code for you guys.', 'start': 18951.103, 'duration': 2.162}, {'end': 18955.347, 'text': 'So that was quick.', 'start': 18954.626, 'duration': 0.721}, {'end': 18961.492, 'text': "that took only about 5 seconds and we have evaluated the model and now it's time for the most important part, guys,", 'start': 18955.347, 'duration': 6.145}], 'summary': 'Reduced loss from 1100 to 130, prediction accuracy increased from 0.02 to 0.0994 in 10 passes.', 'duration': 32.35, 'max_score': 18929.142, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM18929142.jpg'}, {'end': 19028.77, 'src': 'embed', 'start': 18991.786, 'weight': 3, 'content': [{'end': 18994.547, 'text': "Well, this is not a really good case, but okay, that's tolerable.", 'start': 18991.786, 'duration': 2.761}, {'end': 19000.011, 'text': 'and next up we predicted 11.9 when the actual values tell Wow, that is actually really close.', 'start': 18995.007, 'duration': 5.004}, {'end': 19003.034, 'text': 'So next up we have 15.7 versus 9.', 'start': 19000.432, 'duration': 2.602}, {'end': 19006.737, 'text': "Well, this goes on and on for the first 15 and it's actually really really good.", 'start': 19003.034, 'duration': 3.703}, {'end': 19008.238, 'text': 'Well guys pretty well.', 'start': 19007.157, 'duration': 1.081}, {'end': 19012.342, 'text': 'It turns out that there are some relationship between our wines description and its price.', 'start': 19008.318, 'duration': 4.024}, {'end': 19017.106, 'text': 'Well, we might not be able to see it instinctively but our machine learning models certainly can.', 'start': 19012.762, 'duration': 4.344}, {'end': 19022.126, 'text': "So lastly, let's compare the average difference between the actual price and the malls predicted price.", 'start': 19017.924, 'duration': 4.202}, {'end': 19026.449, 'text': 'Well, the average prediction difference is about $10 for every wine bottle.', 'start': 19022.506, 'duration': 3.943}, {'end': 19028.77, 'text': 'Wow, that is really really nice case.', 'start': 19026.469, 'duration': 2.301}], 'summary': 'Predicted wine prices with $10 average difference, showing strong model relationship.', 'duration': 36.984, 'max_score': 18991.786, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM18991786.jpg'}], 'start': 18220.125, 'title': 'Wine data analysis and price prediction models', 'summary': 'Discusses wide and deep models, wine dataset with 12 columns, and the goal of creating a model for identifying wine variety, winery, and location, along with building a wine price prediction model using python, achieving an average prediction difference of $10 per bottle.', 'chapters': [{'end': 18366.395, 'start': 18220.125, 'title': 'Wine data analysis model', 'summary': 'Discusses the wide and deep models, the wine dataset from kaggle with 12 columns, and the goal of creating a model for identifying wine variety, winery, and location based on description, offering opportunities for sentiment analysis and text-related predictive models.', 'duration': 146.27, 'highlights': ["The wine dataset from Kaggle consists of 12 columns, including country, description, designation, points, price, province, region 1, region 2, taster's name, Twitter handle, title, variety, and winery. The dataset contains 12 key columns providing essential information about the wine, including points, price, and variety, which are crucial for the analysis.", 'The overall goal is to create a model that can identify the variety, winery, and location of a wine based on the description alone, offering opportunities for sentiment analysis and other text-related predictive models. The primary objective is to develop a model for identifying wine variety, winery, and location based on description, providing opportunities for sentiment analysis and text-related predictive models.', 'Wide models are models with sparse feature vectors, while deep networks excel in tasks like speech and image recognition. Wide models have sparse feature vectors, mostly consisting of zeros and a few ones, while deep networks excel in speech and image recognition tasks.']}, {'end': 19028.77, 'start': 18366.896, 'title': 'Building a wine price prediction model', 'summary': 'Outlines the process of building a wine price prediction model using python, pandas, numpy, scikit-learn, tensorflow, and keras, achieving an average prediction difference of $10 for every wine bottle after the training and evaluation.', 'duration': 661.874, 'highlights': ['The chapter outlines the process of building a wine price prediction model using Python, Pandas, NumPy, Scikit-learn, TensorFlow, and Keras. It emphasizes the tools and technologies used to build the wine price prediction model.', 'Achieving an average prediction difference of $10 for every wine bottle after the training and evaluation. It quantifies the success of the model in predicting wine prices, highlighting the average prediction difference of $10 per bottle.', 'The training process resulted in the reduction of loss from 1100 to 130 and an increase in prediction accuracy from 0.02 to 0.0994. It showcases the improvement in model performance during the training process, with the loss reducing from 1100 to 130 and the prediction accuracy increasing from 0.02 to 0.0994.', "The model demonstrates a close prediction to the actual values for the first 15 wines from the test dataset. It emphasizes the model's accuracy by showcasing close predictions to the actual values for the first 15 wines from the test dataset.", 'The process involves using Keras and TensorFlow to create a wide and deep model for predicting wine prices. It explains the use of Keras and TensorFlow to create both wide and deep models for predicting wine prices, highlighting the sophisticated modeling approach.']}], 'duration': 808.645, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM18220125.jpg', 'highlights': ["The wine dataset from Kaggle consists of 12 columns, including country, description, designation, points, price, province, region 1, region 2, taster's name, Twitter handle, title, variety, and winery. The dataset contains 12 key columns providing essential information about the wine, including points, price, and variety, which are crucial for the analysis.", 'The overall goal is to create a model that can identify the variety, winery, and location of a wine based on the description alone, offering opportunities for sentiment analysis and other text-related predictive models. The primary objective is to develop a model for identifying wine variety, winery, and location based on description, providing opportunities for sentiment analysis and text-related predictive models.', 'The chapter outlines the process of building a wine price prediction model using Python, Pandas, NumPy, Scikit-learn, TensorFlow, and Keras. It emphasizes the tools and technologies used to build the wine price prediction model.', 'Achieving an average prediction difference of $10 for every wine bottle after the training and evaluation. It quantifies the success of the model in predicting wine prices, highlighting the average prediction difference of $10 per bottle.', 'The training process resulted in the reduction of loss from 1100 to 130 and an increase in prediction accuracy from 0.02 to 0.0994. It showcases the improvement in model performance during the training process, with the loss reducing from 1100 to 130 and the prediction accuracy increasing from 0.02 to 0.0994.', "The model demonstrates a close prediction to the actual values for the first 15 wines from the test dataset. It emphasizes the model's accuracy by showcasing close predictions to the actual values for the first 15 wines from the test dataset.", 'Wide models are models with sparse feature vectors, while deep networks excel in tasks like speech and image recognition. Wide models have sparse feature vectors, mostly consisting of zeros and a few ones, while deep networks excel in speech and image recognition tasks.', 'The process involves using Keras and TensorFlow to create a wide and deep model for predicting wine prices. It explains the use of Keras and TensorFlow to create both wide and deep models for predicting wine prices, highlighting the sophisticated modeling approach.']}, {'end': 21358.912, 'segs': [{'end': 19147.901, 'src': 'embed', 'start': 19123.061, 'weight': 0, 'content': [{'end': 19129.086, 'text': 'Optimal, meaning that the Easter algorithm is sure to find the least cost path from the source to the destination.', 'start': 19123.061, 'duration': 6.025}, {'end': 19135.091, 'text': 'and complete, meaning that it is going to find all the parts that are available to us from the source to the destination.', 'start': 19129.086, 'duration': 6.005}, {'end': 19138.293, 'text': 'So that makes a star the best algorithm, right?', 'start': 19135.791, 'duration': 2.502}, {'end': 19147.901, 'text': 'Well, in most cases, yes, but a star is slow and also the space it requires is a lot, as it saves all the possible parts that are available to us.', 'start': 19138.854, 'duration': 9.047}], 'summary': "A* algorithm ensures least cost path, but it's slow and space-consuming.", 'duration': 24.84, 'max_score': 19123.061, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM19123061.jpg'}, {'end': 19227.586, 'src': 'embed', 'start': 19182.598, 'weight': 1, 'content': [{'end': 19188.802, 'text': 'A star algorithm on the other hand finds the most optimal path that it can take from the source in reaching the destination.', 'start': 19182.598, 'duration': 6.204}, {'end': 19194.867, 'text': 'It knows which is the best part that it can take from its current state and how it needs to reach the destination.', 'start': 19189.243, 'duration': 5.624}, {'end': 19200.046, 'text': "So, now that you know why we choose a star, let's understand a bit of theory about it,", 'start': 19195.563, 'duration': 4.483}, {'end': 19203.589, 'text': 'as it is essential to help you understand how this algorithm works.', 'start': 19200.046, 'duration': 3.543}, {'end': 19209.693, 'text': 'A star as we all know by now is used to find the most optimal path from a source to a destination.', 'start': 19204.329, 'duration': 5.364}, {'end': 19214.416, 'text': 'It optimizes the path by calculating the least distance from one node to the other.', 'start': 19210.233, 'duration': 4.183}, {'end': 19220.561, 'text': 'There is one formula that all of you need to remember as it is the heart and soul of this algorithm.', 'start': 19214.937, 'duration': 5.624}, {'end': 19223.983, 'text': 'F is equal to G plus H.', 'start': 19221.301, 'duration': 2.682}, {'end': 19227.586, 'text': 'Remember this by heart if you want to understand the algorithm properly.', 'start': 19223.983, 'duration': 3.603}], 'summary': 'A* algorithm finds the most optimal path from source to destination by minimizing distance. formula: f = g + h.', 'duration': 44.988, 'max_score': 19182.598, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM19182598.jpg'}, {'end': 19986.944, 'src': 'embed', 'start': 19958.449, 'weight': 3, 'content': [{'end': 19965.692, 'text': "The planet was actually discovered through the NASA's Kepler space telescope by using machine learning and, according to a news article from NASA,", 'start': 19958.449, 'duration': 7.243}, {'end': 19971.455, 'text': "Kepler's four-year data set consists 35,000 possible planetary signals and automated tests,", 'start': 19965.692, 'duration': 5.763}, {'end': 19976.117, 'text': 'and sometimes human eyes are used to verify the most promising signals in the data.', 'start': 19971.455, 'duration': 4.662}, {'end': 19986.944, 'text': 'However, the weakest signal often are missed using these methods to shallow and Vandenberg thought they could be more interesting exoplanet discoveries faintly lurking in the data.', 'start': 19976.758, 'duration': 10.186}], 'summary': "Nasa's kepler telescope discovered a planet using machine learning, with a dataset of 35,000 possible planetary signals and automated tests.", 'duration': 28.495, 'max_score': 19958.449, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM19958449.jpg'}, {'end': 20153.639, 'src': 'embed', 'start': 20124.864, 'weight': 4, 'content': [{'end': 20131.689, 'text': 'Now talking about the communication part the NASA spacecraft typically rely on human control radio systems to communicate with the Earth.', 'start': 20124.864, 'duration': 6.825}, {'end': 20137.036, 'text': 'and as collection of space data increases, NASA looks to cognitive radio,', 'start': 20132.395, 'duration': 4.641}, {'end': 20143.337, 'text': 'the infusion of artificial intelligence into space communications Network to meet the demand and increase efficiency.', 'start': 20137.036, 'duration': 6.301}, {'end': 20146.618, 'text': 'So software defined radios, like cognitive radio,', 'start': 20143.817, 'duration': 2.801}, {'end': 20153.639, 'text': 'use artificial intelligence to imply under utilized portions of the electromagnetic spectrum without the human intervention,', 'start': 20146.618, 'duration': 7.021}], 'summary': 'Nasa aims to use cognitive radio and ai to improve space communication efficiency.', 'duration': 28.775, 'max_score': 20124.864, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM20124864.jpg'}, {'end': 21328.742, 'src': 'embed', 'start': 21302.304, 'weight': 5, 'content': [{'end': 21309.549, 'text': 'We will shift gears a bit and study some of the fundamental concepts that prevail in the world of reinforcement learning and Q learning.', 'start': 21302.304, 'duration': 7.245}, {'end': 21312.391, 'text': "So first of all, we'll start with the Bellman equation.", 'start': 21310.029, 'duration': 2.362}, {'end': 21320.917, 'text': 'Now consider the following square of rooms, which is analogous to the actual environment from our original problem, but without the barriers.', 'start': 21313.051, 'duration': 7.866}, {'end': 21328.742, 'text': 'Now suppose a robot needs to go to the room marked in the green from its current position A using the specified direction.', 'start': 21321.517, 'duration': 7.225}], 'summary': 'Introduction to fundamental concepts in reinforcement learning and q learning, including the bellman equation and a scenario involving a robot navigating a square of rooms.', 'duration': 26.438, 'max_score': 21302.304, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM21302304.jpg'}], 'start': 19035.066, 'title': 'Ai and algorithms in space science', 'summary': "Covers a* algorithm, dijkstra's algorithm, and their comparison, along with ai's role in space science, cognitive computing, and reinforcement learning in robotics, highlighting key attributes and applications in various fields.", 'chapters': [{'end': 19181.758, 'start': 19035.066, 'title': 'Understanding a star algorithm', 'summary': 'Explains the a star algorithm, an advanced breadth-first search algorithm that ensures finding the least cost path from source to destination, and compares it with other algorithms, highlighting its optimality and completeness, despite being slower and requiring more space.', 'duration': 146.692, 'highlights': ['A star algorithm is optimal as well as a complete, ensuring the least cost path from source to destination and finding all available paths. The A star algorithm guarantees finding the least cost path from source to destination and ensures finding all available paths, making it optimal and complete.', "A star algorithm is slower and requires more space compared to faster algorithms, such as Dijkstra's algorithm. A star algorithm, while being optimal and complete, is slower and requires more space, which gives faster algorithms like Dijkstra's algorithm an advantage.", "Dijkstra's algorithm finds all paths without determining the most optimal one, leading to unoptimized working and unnecessary computations. Dijkstra's algorithm finds all paths without determining the most optimal one, resulting in unoptimized working and unnecessary computations."]}, {'end': 19450.336, 'start': 19182.598, 'title': 'A* algorithm: finding optimal paths', 'summary': 'Explains the a* algorithm, which optimizes the path from a source to a destination by calculating the least distance using the formula f = g + h, and demonstrates its application through a simple example.', 'duration': 267.738, 'highlights': ['The A* algorithm optimizes the path by calculating the least distance from one node to the other using the formula F = G + H. It finds the most optimal path from the source to the destination by calculating the least distance using the formula F = G + H.', 'The formula F = G + H is the heart and soul of the A* algorithm. The formula F = G + H is the fundamental equation of the A* algorithm and is essential for understanding its operation.', 'The parameters G and H represent the cost of moving from one node to another and the heuristic estimate from the current node to the destination, respectively. Parameter G represents the cost of moving from one node to another, while parameter H is the heuristic estimate from the current node to the destination.', 'Demonstrating the algorithm through a simple example with vertices S, A, B, and E, the chapter showcases the application of the A* algorithm in finding the most optimal path. The chapter demonstrates the application of the A* algorithm through a simple example with vertices S, A, B, and E, showcasing how it finds the most optimal path.', 'The A* algorithm uses open and closed lists to track nodes, with the open list representing the nodes currently being visited and the closed list containing nodes that have not been visited but will be calculated. The A* algorithm uses open and closed lists to track nodes, with the open list representing the nodes currently being visited and the closed list containing nodes that have not been visited but will be calculated.']}, {'end': 19898.04, 'start': 19450.979, 'title': 'Pathfinding algorithms: dijkstra vs a*', 'summary': "Introduces dijkstra's algorithm and a* algorithm, demonstrating how a* overcomes dijkstra's failure to find the shortest path, though at the cost of increased time and computations, making it a valuable but slower option for pathfinding.", 'duration': 447.061, 'highlights': ["A* algorithm finds the shortest path where Dijkstra fails A* algorithm is able to accomplish pathfinding where Dijkstra's algorithm fails, as demonstrated in the given example.", 'A* algorithm is slower but effective for pathfinding A* algorithm is effective in finding the shortest path, but it is slower and requires more computations, making it a valuable but slower option for pathfinding.', "Explanation of Dijkstra's algorithm The chapter explains Dijkstra's algorithm, which involves mapping all points to infinity, finding necessary lists, and determining distances between nodes to find the shortest path."]}, {'end': 20503.035, 'start': 19898.561, 'title': 'Ai in space science', 'summary': 'Discusses how ai has shaped space science, highlighting the significant role of ai in space exploration, global navigation, communication, and future applications, such as asteroid identification and life discovery on nearby planets.', 'duration': 604.474, 'highlights': ["NASA's Kepler space telescope used machine learning to discover Kepler-90i, and a neural network identified true planets and false positives 96% of the time, showcasing AI's role in exoplanet discovery.", "Global Navigation Satellite System (GNSS) data, processed using AI and machine learning, can detect real-time events like tsunamis and study parameters such as temperature and gases, demonstrating AI's impact on disaster detection and analysis.", "Cognitive radio, infused with AI, can optimize space communications network efficiency, prioritize data transmission, and mitigate radiation damage during severe space weather events, showcasing AI's role in enhancing space communication.", "AI algorithms have been developed to render asteroids in as little as four days and fill in missing data from broken sensors, exemplifying AI's potential in asteroid identification, shape modeling, and data analysis in space science.", 'The role of robots and AI in space exploration is emphasized, highlighting the potential for AI-powered robots to perform tasks in space missions, while humans can monitor the program remotely from Earth, indicating the future integration of AI and robotics in space exploration.']}, {'end': 20999.832, 'start': 20503.715, 'title': 'Understanding cognitive computing', 'summary': 'Introduces cognitive computing as smart decision support systems that use better data and algorithms to understand and simulate human behavior, and discusses its key attributes, differences from ai, and applications in smart iot, ai-enabled cybersecurity, content ai, cognitive analytics in healthcare, and intent-based nlp.', 'duration': 496.117, 'highlights': ['Cognitive computing refers to smart decision support systems using better data and algorithms to understand and simulate human behavior. Cognitive computing uses better data and algorithms to understand and simulate human behavior to facilitate human intelligence.', 'Applications of cognitive computing include smart IoT, AI-enabled cybersecurity, content AI, cognitive analytics in healthcare, and intent-based NLP. Cognitive computing has applications in smart IoT, AI-enabled cybersecurity, content AI, cognitive analytics in healthcare, and intent-based NLP.', 'Cognitive computing systems synthesize data from various sources, using self-learning technologies, data mining, pattern recognition, and natural language processing. Cognitive computing systems synthesize data from various sources using self-learning technologies, data mining, pattern recognition, and natural language processing.', 'Cognitive computing focuses on mimicking human behavior and reasoning, while AI augments human thinking and finds patterns to learn or reveal hidden information. Cognitive computing focuses on mimicking human behavior and reasoning, while AI augments human thinking and finds patterns to learn or reveal hidden information.', 'Intent-based NLP is an application of cognitive computing that can help businesses become more analytical in their approach to management and decision-making. Intent-based NLP is an application of cognitive computing that helps businesses become more analytical in their approach to management and decision-making.']}, {'end': 21358.912, 'start': 20999.832, 'title': 'Reinforcement learning in robotics', 'summary': 'Discusses the application of reinforcement learning in training autonomous robots to navigate an automobile factory by defining the components of a reinforcement learning solution, mapping locations to states, and constructing a reward table with prioritized locations, leading to a formal definition of the vital components for the problem, and introducing the fundamental concepts of reinforcement learning and q learning through the bellman equation.', 'duration': 359.08, 'highlights': ['The chapter discusses the application of reinforcement learning in training autonomous robots to navigate an automobile factory by defining the components of a reinforcement learning solution, mapping locations to states, and constructing a reward table with prioritized locations. The chapter highlights the application of reinforcement learning in training autonomous robots to navigate an automobile factory by defining the components of a reinforcement learning solution, mapping locations to states, and constructing a reward table with prioritized locations. This provides a practical example of the application of reinforcement learning in a real-world scenario, showcasing the relevance of reinforcement learning in robotics.', 'The chapter introduces the fundamental concepts of reinforcement learning and Q learning through the Bellman equation. The chapter introduces the fundamental concepts of reinforcement learning and Q learning through the Bellman equation, providing a foundational understanding of key concepts in reinforcement learning and Q learning. This serves as an essential building block for comprehending the theoretical framework behind reinforcement learning.']}], 'duration': 2323.846, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM19035066.jpg', 'highlights': ['A* algorithm guarantees finding the least cost path from source to destination and ensures finding all available paths, making it optimal and complete.', 'A* algorithm optimizes the path by calculating the least distance from one node to the other using the formula F = G + H.', "A* algorithm finds the shortest path where Dijkstra fails A* algorithm is able to accomplish pathfinding where Dijkstra's algorithm fails, as demonstrated in the given example.", "NASA's Kepler space telescope used machine learning to discover Kepler-90i, and a neural network identified true planets and false positives 96% of the time, showcasing AI's role in exoplanet discovery.", 'Cognitive computing refers to smart decision support systems using better data and algorithms to understand and simulate human behavior.', 'The chapter discusses the application of reinforcement learning in training autonomous robots to navigate an automobile factory by defining the components of a reinforcement learning solution, mapping locations to states, and constructing a reward table with prioritized locations.']}, {'end': 23616.573, 'segs': [{'end': 21403.18, 'src': 'embed', 'start': 21359.58, 'weight': 3, 'content': [{'end': 21363.961, 'text': 'Now, consider the robot starts at this location rather than its previous one.', 'start': 21359.58, 'duration': 4.381}, {'end': 21367.642, 'text': 'Now the robot now sees footprints in two different directions.', 'start': 21364.641, 'duration': 3.001}, {'end': 21373.744, 'text': 'It is therefore unable to decide which way to go in order to get the destination, which is the green room.', 'start': 21368.202, 'duration': 5.542}, {'end': 21379.705, 'text': 'It happens primarily because the robot does not have a way to remember the directions to proceed.', 'start': 21374.384, 'duration': 5.321}, {'end': 21383.466, 'text': 'So our job now is to enable the robot with the memory.', 'start': 21380.245, 'duration': 3.221}, {'end': 21386.387, 'text': 'Now, this is where the Bellman equation comes into play.', 'start': 21384.106, 'duration': 2.281}, {'end': 21392.295, 'text': 'So as you can see here, the main reason of the Bellarmine equation is to enable the reward with the memory.', 'start': 21387, 'duration': 5.295}, {'end': 21393.698, 'text': "That's the thing we're going to use.", 'start': 21392.355, 'duration': 1.343}, {'end': 21403.18, 'text': 'So the equation goes something like this V of s gives maximum of a R of s, comma a plus gamma of V s dash.', 'start': 21394.318, 'duration': 8.862}], 'summary': 'Robot needs memory to navigate to green room; bellman equation enables reward with memory.', 'duration': 43.6, 'max_score': 21359.58, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM21359580.jpg'}, {'end': 21602.537, 'src': 'embed', 'start': 21520.53, 'weight': 6, 'content': [{'end': 21526.035, 'text': 'So this is how the table looks with some value footprints computed from the Bellman equation.', 'start': 21520.53, 'duration': 5.505}, {'end': 21534.781, 'text': 'A couple of things to notice here is that the max function helps the robot to always choose the state that gives it the maximum value of being in that state.', 'start': 21526.893, 'duration': 7.888}, {'end': 21540.927, 'text': 'Now the discount factor gamma notifies the robot about how far it is from the destination.', 'start': 21535.301, 'duration': 5.626}, {'end': 21546.812, 'text': 'This is typically specified by the developer of the algorithm that would be installed in the robot.', 'start': 21541.447, 'duration': 5.365}, {'end': 21551.657, 'text': 'Now the other states can also be given their respective values in a similar way.', 'start': 21547.293, 'duration': 4.364}, {'end': 21565.613, 'text': 'So as you can see here the boxes adjacent to the green one have one and if we move away from one we get 0.9 0.81 0.729 and finally we reach 0.66.', 'start': 21552.324, 'duration': 13.289}, {'end': 21571.196, 'text': 'Now. the robot now can proceed its way through the green room utilizing these value footprints,', 'start': 21565.613, 'duration': 5.583}, {'end': 21575.139, 'text': "even if it's dropped at any arbitrary room in the given location.", 'start': 21571.196, 'duration': 3.943}, {'end': 21582.522, 'text': 'Now, if a robot lands up in the highlighted sky blue area, it will still find two options to choose from.', 'start': 21575.777, 'duration': 6.745}, {'end': 21590.128, 'text': 'But eventually, either of the paths will be good enough for the robot to take because of the way the value footprints are now laid out.', 'start': 21583.163, 'duration': 6.965}, {'end': 21597.433, 'text': 'Now, one thing to note here is that the Bellman equation is one of the key equations in the world of reinforcement learning and Q learning.', 'start': 21590.928, 'duration': 6.505}, {'end': 21602.537, 'text': 'So if we think realistically, our surroundings do not always work in the way we expect.', 'start': 21598.034, 'duration': 4.503}], 'summary': 'Value footprints guide robot to choose optimal state; bellman equation crucial in reinforcement learning.', 'duration': 82.007, 'max_score': 21520.53, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM21520530.jpg'}, {'end': 21657.115, 'src': 'embed', 'start': 21637.4, 'weight': 0, 'content': [{'end': 21649.829, 'text': "Now, let's now consider the robot has a slight chance of dysfunctioning and might take the left or the right or the bottom turn instead of digging the upper turn in order to get to the green room from where it is now,", 'start': 21637.4, 'duration': 12.429}, {'end': 21650.83, 'text': 'which is the red room.', 'start': 21649.829, 'duration': 1.001}, {'end': 21657.115, 'text': 'Now the question is how do we enable the robot to handle this when it is out in the given environment right?', 'start': 21651.411, 'duration': 5.704}], 'summary': 'Robot may malfunction, taking different turns to reach green from red room.', 'duration': 19.715, 'max_score': 21637.4, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM21637400.jpg'}, {'end': 21825.39, 'src': 'embed', 'start': 21797.947, 'weight': 11, 'content': [{'end': 21803.634, 'text': 'we essentially mean that there is an 80% chance that the robot will take the upper turn.', 'start': 21797.947, 'duration': 5.687}, {'end': 21815.625, 'text': 'Now, if you put all the required values in our equation, we get V of S is equal to maximum of R of S, comma A plus comma of 0.8 into V of room up,', 'start': 21804.44, 'duration': 11.185}, {'end': 21825.39, 'text': 'plus 0.1 into V of room down, 0.03 into room of V of room left, plus 0.03 into V of room right.', 'start': 21815.625, 'duration': 9.765}], 'summary': 'There is an 80% chance that the robot will take the upper turn.', 'duration': 27.443, 'max_score': 21797.947, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM21797947.jpg'}, {'end': 21894.527, 'src': 'embed', 'start': 21845.91, 'weight': 1, 'content': [{'end': 21849.092, 'text': 'We are only rewarding the robot when it gets to the destination.', 'start': 21845.91, 'duration': 3.182}, {'end': 21857.229, 'text': 'Now, ideally there should be a reward for each action the robot takes to help it better assess the quality of the actions.', 'start': 21849.801, 'duration': 7.428}, {'end': 21860.412, 'text': 'but the rewards need not to be always be the same.', 'start': 21857.229, 'duration': 3.183}, {'end': 21865.858, 'text': 'but it is much better than having some amount of reward for the actions than having no rewards at all.', 'start': 21860.412, 'duration': 5.446}, {'end': 21870.022, 'text': 'Right and this idea is known as the living penalty.', 'start': 21866.058, 'duration': 3.964}, {'end': 21871.539, 'text': 'In reality,', 'start': 21870.899, 'duration': 0.64}, {'end': 21880.302, 'text': 'the reward system can be very complex and particularly modeling sparse rewards is an active area of research in the domain of reinforcement learning.', 'start': 21871.539, 'duration': 8.763}, {'end': 21887.325, 'text': "So by now we have got the equation which we have is so what we're going to do is now transition to Q learning.", 'start': 21880.902, 'duration': 6.423}, {'end': 21894.527, 'text': 'So this equation gives us the value of going to a particular state taking the stochasticity of the environment into account.', 'start': 21887.945, 'duration': 6.582}], 'summary': 'Sparse rewards in reinforcement learning can be complex; q learning helps assess actions.', 'duration': 48.617, 'max_score': 21845.91, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM21845910.jpg'}, {'end': 22462.47, 'src': 'embed', 'start': 22430.466, 'weight': 14, 'content': [{'end': 22437.708, 'text': "So what we're going to do is initialize the Q values to be 0 and in the Q learning process what you can see here.", 'start': 22430.466, 'duration': 7.242}, {'end': 22442.569, 'text': "We are taking I in range 1000 and we're going to pick up a state randomly.", 'start': 22437.728, 'duration': 4.841}, {'end': 22446.09, 'text': "So we're going to use the NP dot, random Rand int,", 'start': 22443.069, 'duration': 3.021}, {'end': 22462.47, 'text': "and for traversing through the neighbor location in the same maze we're going to iterate through the new reward matrix and get the actions which are greater than zero and after that what we're going to do is pick an action randomly from the list of the playable actions in years to the next state.", 'start': 22447.166, 'duration': 15.304}], 'summary': 'Initializing q values to 0, using q learning to iterate through 1000 states and pick actions randomly from playable actions.', 'duration': 32.004, 'max_score': 22430.466, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM22430466.jpg'}, {'end': 22569.028, 'src': 'embed', 'start': 22539.171, 'weight': 2, 'content': [{'end': 22542.772, 'text': 'So as you can see here, we get L9, L8, L5, L2 and L1.', 'start': 22539.171, 'duration': 3.601}, {'end': 22551.536, 'text': 'And if you have a look at the image here we have, if we start from L9 to L1, we got L8, L5, L2, L1.', 'start': 22544.033, 'duration': 7.503}, {'end': 22552.316, 'text': 'L8, L5, L2, L1.', 'start': 22551.696, 'duration': 0.62}, {'end': 22560.126, 'text': 'That would yield us the maximum value or the maximum reward for the robot.', 'start': 22555.436, 'duration': 4.69}, {'end': 22569.028, 'text': 'so now we have come to the end of this Q learning session and I hope you got to know what exactly is Q learning with the analogy all the way,', 'start': 22561.167, 'duration': 7.861}], 'summary': 'Q learning yielded maximum reward, understanding q learning analogy.', 'duration': 29.857, 'max_score': 22539.171, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM22539171.jpg'}, {'end': 22686.063, 'src': 'embed', 'start': 22646.319, 'weight': 15, 'content': [{'end': 22652.361, 'text': 'that means we are working with jugs with no marking or labels, and also we do not have any measuring devices.', 'start': 22646.319, 'duration': 6.042}, {'end': 22660.506, 'text': 'Now, imagine that if we had a jug which had labels and also a measuring device, we could easily say that, yes,', 'start': 22653.001, 'duration': 7.505}, {'end': 22663.749, 'text': 'this jug is 2 liters filled or 3 liters filled.', 'start': 22660.506, 'duration': 3.243}, {'end': 22667.131, 'text': 'We can easily understand how much quantity is there in this jug,', 'start': 22663.909, 'duration': 3.222}, {'end': 22672.935, 'text': 'but we do not have any labels or any measuring devices to measure the quantity in this jug.', 'start': 22667.131, 'duration': 5.804}, {'end': 22677.218, 'text': 'This is what the problem is and we will find various solutions to this problem.', 'start': 22673.415, 'duration': 3.803}, {'end': 22681.261, 'text': 'But before that we will move on to the importance of water jug problem.', 'start': 22677.798, 'duration': 3.463}, {'end': 22686.063, 'text': "So suppose I start from my house and I want to go to McDonald's to have a burger.", 'start': 22681.781, 'duration': 4.282}], 'summary': 'Addressing the challenge of measuring unknown liquid quantities in unmarked jugs, exploring solutions and highlighting the importance of the water jug problem.', 'duration': 39.744, 'max_score': 22646.319, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM22646319.jpg'}], 'start': 21359.58, 'title': 'Q-learning in robotics', 'summary': 'Explores q-learning algorithms in robotics, covering topics like decision-making challenges, reinforcement learning basics, implementing q-learning in robot navigation, finding optimal routes, and solving the water jug problem in ai, with a focus on memory, rewards, discount factor, and markov decision processes.', 'chapters': [{'end': 21403.18, 'start': 21359.58, 'title': "Robot's decision making process", 'summary': 'Discusses the challenges faced by a robot in decision-making due to lack of memory, and introduces the bellman equation as a solution to enable the robot with memory to make decisions, with a focus on maximizing rewards.', 'duration': 43.6, 'highlights': ['The main reason of the Bellman equation is to enable the reward with the memory. The Bellman equation is introduced to enable the robot with memory to make decisions, thereby enhancing its ability to maximize rewards.', 'The robot now sees footprints in two different directions, unable to decide which way to go. The robot faces the challenge of being unable to make a decision due to lack of memory, as it sees footprints in two different directions.', "The robot does not have a way to remember the directions to proceed. The robot's inability to remember directions hinders its decision-making process, leading to uncertainty in choosing the correct path."]}, {'end': 21748.55, 'start': 21403.18, 'title': 'Reinforcement learning basics', 'summary': 'Introduces the concept of value footprints and the bellman equation in reinforcement learning, highlighting the role of discount factor gamma and the application of markov decision process in handling stochasticity in decision-making.', 'duration': 345.37, 'highlights': ['The Bellman equation is a key equation in reinforcement learning and Q learning, guiding the robot in choosing the state that yields the maximum value and notifying it about the distance from the destination. Key equation in reinforcement learning and Q learning; Guides robot in choosing state with maximum value; Notifies robot about distance from destination.', 'The application of the discount factor gamma helps in computing the value footprints, demonstrating the decreasing values as the robot moves away from the green room. Discount factor gamma aids in computing value footprints; Values decrease as robot moves away from green room.', 'The introduction of stochasticity in decision-making is addressed through the Markov decision process, providing a mathematical framework for modeling situations where outcomes are partly random and partly under the control of the decision maker. Markov decision process models partly random outcomes; Provides mathematical framework for decision-making; Introduces stochasticity in decision-making.']}, {'end': 22303.984, 'start': 21748.55, 'title': 'Implementing q learning in robot navigation', 'summary': 'Discusses incorporating probabilities into the navigation equation, introducing the idea of living penalty for rewarding robot actions, transitioning to q learning to quantify the quality of actions, and implementing the q learning algorithm with parameters like gamma and alpha.', 'duration': 555.434, 'highlights': ['Introduction of Living Penalty for Robot Actions The concept of living penalty is introduced to reward the robot for its actions, with the suggestion that having some amount of reward for the actions is better than having no rewards at all.', 'Transition to Q Learning The chapter transitions to Q learning, which involves assessing the quality of actions taken to move to a state, rather than determining the possible value of the state being moved to.', 'Introduction of Temporal Difference in Q Values Temporal difference is introduced as a component to help the robot calculate the Q values with respect to the changes in the environment over time, with an equation for updating the Q values based on the learning rate and maximum Q values.', 'Implementation of Q Learning Algorithm The chapter discusses the implementation of the Q learning algorithm, including defining states and actions, initializing parameters like gamma and alpha, and creating the reward table to facilitate the robot navigation.']}, {'end': 22538.41, 'start': 22304.665, 'title': 'Q-learning algorithm for optimal route', 'summary': 'Discusses the q-learning algorithm for finding the optimal route in a warehouse, with a focus on initializing q values, updating them using the bellman equation, and obtaining the optimal route from a starting location to an end location.', 'duration': 233.745, 'highlights': ['Initializing Q values to be all zeros for the Q-learning process During the Q-learning process, the Q values are initialized to be all zeros, which sets the initial state for the algorithm.', 'Updating Q values using the Bellman equation The Q values are updated using the Bellman equation, which involves computing the temporal difference and using np.argmax to update the Q values for the current state.', 'Obtaining the optimal route from a starting location to an end location The algorithm fetches the starting state, fetches the highest Q value pertaining to the starting state, uses the state to location function to find the corresponding letter, and updates the starting location for the next iteration to ultimately return the optimal route.']}, {'end': 23169.048, 'start': 22539.171, 'title': 'Q learning and water jug problem', 'summary': 'Covers q learning with an analogy, the importance of the water jug problem in artificial intelligence, and various solutions to the problem, with a focus on the state space representation and production rules used in artificial intelligence.', 'duration': 629.877, 'highlights': ['The chapter covers Q learning with an analogy, the importance of the water jug problem in artificial intelligence, and various solutions to the problem, with a focus on the state space representation and production rules used in artificial intelligence. It discusses the Q learning with an analogy, the importance of the water jug problem in artificial intelligence, and various solutions to the problem, focusing on state space representation and production rules used in artificial intelligence.', 'It explains the process of reaching the goal state in the water jug problem, including different possible solutions and the state space representation of achieving the goal state. It details the process of reaching the goal state in the water jug problem, presenting different possible solutions and state space representation of achieving the goal state.', 'The chapter also discusses the assumptions and considerations in the water jug problem, such as the unlimited supply of water, pouring water out of a jug to the ground, and the absence of measuring devices. It covers the assumptions and considerations in the water jug problem, including the unlimited supply of water, pouring water out of a jug to the ground, and the absence of measuring devices.']}, {'end': 23616.573, 'start': 23169.649, 'title': 'Jug filling rules in artificial intelligence', 'summary': 'Discusses the rules for filling and emptying jugs a and b with conditions such as a plus b should be greater than or equal to 4, and b should be greater than 0. it also explains the application of these rules to reach the goal state of filling two liters in jug a and zero or any quantity in jug b.', 'duration': 446.924, 'highlights': ['The chapter discusses the rules for filling and emptying jugs A and B with specific conditions such as A plus B should be greater than or equal to 4, and B should be greater than 0. The chapter emphasizes the specific conditions for filling and emptying jugs A and B, such as A plus B should be greater than or equal to 4, and B should be greater than 0.', 'It explains the application of these rules to reach the goal state of filling two liters in jug A and zero or any quantity in jug B. The application of rules is detailed in achieving the goal state of filling two liters in jug A and zero or any quantity in jug B.', 'The chapter demonstrates the execution of specific rules, such as refilling jug B with three liters of water, and transferring water from jug B to jug A. The chapter demonstrates the execution of specific rules, such as refilling jug B with three liters of water, and transferring water from jug B to jug A.']}], 'duration': 2256.993, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM21359580.jpg', 'highlights': ["The Bellman equation enhances the robot's ability to maximize rewards with memory.", "The robot's inability to remember directions hinders its decision-making process.", 'The introduction of stochasticity in decision-making is addressed through the Markov decision process.', 'Discount factor gamma aids in computing value footprints; Values decrease as robot moves away from green room.', 'The concept of living penalty is introduced to reward the robot for its actions.', 'The chapter transitions to Q learning, which involves assessing the quality of actions taken to move to a state.', 'Temporal difference is introduced to help the robot calculate the Q values with respect to the changes in the environment over time.', 'The Q learning algorithm discusses defining states and actions, initializing parameters like gamma and alpha, and creating the reward table.', 'The Q values are initialized to be all zeros, setting the initial state for the algorithm.', 'The Q values are updated using the Bellman equation, involving computing the temporal difference and using np.argmax.', 'The algorithm fetches the starting state, fetches the highest Q value, uses the state to location function, and updates the starting location to return the optimal route.', 'The chapter covers Q learning with an analogy, the importance of the water jug problem in artificial intelligence, and various solutions to the problem.', 'It details the process of reaching the goal state in the water jug problem, presenting different possible solutions and state space representation.', 'It covers the assumptions and considerations in the water jug problem, including the unlimited supply of water, pouring water out of a jug to the ground, and the absence of measuring devices.', 'The chapter emphasizes the specific conditions for filling and emptying jugs A and B, such as A plus B should be greater than or equal to 4, and B should be greater than 0.', 'The application of rules is detailed in achieving the goal state of filling two liters in jug A and zero or any quantity in jug B.', 'The chapter demonstrates the execution of specific rules, such as refilling jug B with three liters of water, and transferring water from jug B to jug A.']}, {'end': 24853.722, 'segs': [{'end': 23887.894, 'src': 'embed', 'start': 23855.479, 'weight': 3, 'content': [{'end': 23861.541, 'text': "and that's what we have gone through, and we have started with the start state at 0, 0, and the goal state was 0 and 4,", 'start': 23855.479, 'duration': 6.062}, {'end': 23866.143, 'text': 'to get exactly 4 liters of capacity or 4 liters of water in jug B.', 'start': 23861.541, 'duration': 4.602}, {'end': 23868.508, 'text': 'Okay So this is how the code runs.', 'start': 23866.143, 'duration': 2.365}, {'end': 23875.69, 'text': 'You have to apply the logic here like when this iterates it just fills the jug B jug A and then it fills the jug B.', 'start': 23868.548, 'duration': 7.142}, {'end': 23878.431, 'text': 'So this is how you simply implement it in Python.', 'start': 23875.69, 'duration': 2.741}, {'end': 23887.894, 'text': 'Also, we can apply various algorithms like breadth-first search or depth-first search to find the best optimal search space or the state space for this problem.', 'start': 23878.591, 'duration': 9.303}], 'summary': 'Using python, the code navigates from 0,0 to 0,4, filling jug a and b iteratively to achieve 4 liters, while employing algorithms like breadth-first search or depth-first search.', 'duration': 32.415, 'max_score': 23855.479, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM23855479.jpg'}, {'end': 24016.117, 'src': 'embed', 'start': 23990.61, 'weight': 4, 'content': [{'end': 23996.774, 'text': 'They released it to the public, calling it Playground, where a lot of developers used it for their daily tasks.', 'start': 23990.61, 'duration': 6.164}, {'end': 24001.257, 'text': 'ChatGPT has been another implementation of this.', 'start': 23998.455, 'duration': 2.802}, {'end': 24007.333, 'text': 'Opening AI took a year to make this model faster and more accessible to the general public.', 'start': 24002.21, 'duration': 5.123}, {'end': 24012.055, 'text': 'And when they released it in November 2022, the crowd went nuts.', 'start': 24007.733, 'duration': 4.322}, {'end': 24016.117, 'text': 'The site gained more than a million users in just five days.', 'start': 24013.115, 'duration': 3.002}], 'summary': "Opening ai's playground gained over a million users in 5 days after its november 2022 release.", 'duration': 25.507, 'max_score': 23990.61, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM23990610.jpg'}, {'end': 24099.301, 'src': 'embed', 'start': 24071.886, 'weight': 2, 'content': [{'end': 24078.09, 'text': 'ChatGPT has also helped us give you better, more optimized content while reducing our workload.', 'start': 24071.886, 'duration': 6.204}, {'end': 24084.435, 'text': 'By the way, did you know that ChatGPT can explain stuff better than most university professors?', 'start': 24078.891, 'duration': 5.544}, {'end': 24087.917, 'text': "I guess it's time we contemplated our learning methods.", 'start': 24085.495, 'duration': 2.422}, {'end': 24092.356, 'text': 'Marketing and sales has also been much easier than before.', 'start': 24089.013, 'duration': 3.343}, {'end': 24094.377, 'text': 'Think about it like this.', 'start': 24093.276, 'duration': 1.101}, {'end': 24099.301, 'text': 'If you want to make a customized sales pitch, all you need to do is enter the details.', 'start': 24094.938, 'duration': 4.363}], 'summary': 'Chatgpt has optimized content, reduced workload, and improved marketing and sales processes.', 'duration': 27.415, 'max_score': 24071.886, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM24071886.jpg'}, {'end': 24236.577, 'src': 'embed', 'start': 24213.613, 'weight': 6, 'content': [{'end': 24220.799, 'text': 'ChatGPT eased through it and also provided a sample snippet which I can directly copy and paste into my editor.', 'start': 24213.613, 'duration': 7.186}, {'end': 24222.881, 'text': 'But this is not the only thing.', 'start': 24221.58, 'duration': 1.301}, {'end': 24226.352, 'text': 'It can also give you complete guides on how to do something.', 'start': 24223.43, 'duration': 2.922}, {'end': 24236.577, 'text': 'Like for example, let me type, using Python, help me fetch data for Nifty Bank for the past three years.', 'start': 24227.032, 'duration': 9.545}], 'summary': 'Chatgpt provides sample snippet for easy use and complete guides for various tasks, like fetching data using python.', 'duration': 22.964, 'max_score': 24213.613, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM24213613.jpg'}, {'end': 24589.214, 'src': 'embed', 'start': 24561.497, 'weight': 0, 'content': [{'end': 24568.784, 'text': "Wouldn't it be cool to look at some of the factors or elements that led to such a widespread adoption of artificial intelligence in cultures?", 'start': 24561.497, 'duration': 7.287}, {'end': 24572.386, 'text': "Well, first up, we're going to take a look at advancement in science.", 'start': 24569.424, 'duration': 2.962}, {'end': 24577.068, 'text': 'Movies like the one you see on the screen came out before World War I.', 'start': 24572.806, 'duration': 4.262}, {'end': 24583.591, 'text': 'And they inspired a whole generation of scientists who did some amazing work in the field of computer science and AI.', 'start': 24577.068, 'duration': 6.523}, {'end': 24589.214, 'text': 'So what were the advancements that led to AI? The first computer was made in 1946 and was called ENIAC.', 'start': 24584.031, 'duration': 5.183}], 'summary': 'Advancements in science, like the first computer in 1946, inspired ai adoption.', 'duration': 27.717, 'max_score': 24561.497, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM24561497.jpg'}, {'end': 24719.281, 'src': 'embed', 'start': 24695.546, 'weight': 1, 'content': [{'end': 24702.231, 'text': 'So this was the first element, advancement in science and technology that influenced the culture around AI today.', 'start': 24695.546, 'duration': 6.685}, {'end': 24705.994, 'text': "What's the second one? It is books and literature on AI.", 'start': 24702.611, 'duration': 3.383}, {'end': 24711.958, 'text': 'Literature can range from the ones that deal in facts, such as academic, scientific or research papers,', 'start': 24706.414, 'duration': 5.544}, {'end': 24715.821, 'text': 'to the ones that are very imaginative and completely fictional, like comics.', 'start': 24711.958, 'duration': 3.863}, {'end': 24719.281, 'text': 'Inspired by the advancement in science and technology.', 'start': 24716.32, 'duration': 2.961}], 'summary': 'Advancement in science & technology and literature influenced ai culture.', 'duration': 23.735, 'max_score': 24695.546, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM24695546.jpg'}], 'start': 23616.573, 'title': 'Water jug problem and chatgpt', 'summary': "Discusses solving the water jug problem with python, reaching the goal state of filling jug b with 4 liters and jug a with 0, and highlights the impact of chatgpt's release with over a million users in five days, boosting productivity for developers and content creators, easing marketing and sales efforts, and serving as a tool for psychologists and psychiatrists.", 'chapters': [{'end': 23989.75, 'start': 23616.573, 'title': 'Water jug problem: solving with python', 'summary': 'Discusses solving the water jug problem with python, applying specific rules to reach the goal state of filling jug b with 4 liters and jug a with 0, and mentions the potential use of chatgpt in various fields.', 'duration': 373.177, 'highlights': ['The chapter discusses solving the water jug problem with Python, applying specific rules to reach the goal state of filling jug B with 4 liters and jug A with 0. Solving the water jug problem with Python, applying specific rules, reaching the goal state of filling jug B with 4 liters and jug A with 0.', 'The potential use of ChatGPT in various fields, including integration into Microsoft Teams for note-taking and task recommendation, passing US medical and law exams, and causing concern among professionals about potential job displacement. Potential use of ChatGPT in various fields, integration into Microsoft Teams, passing US medical and law exams, concern among professionals about job displacement.']}, {'end': 24490.162, 'start': 23990.61, 'title': 'Chatgpt: revolutionizing business operations', 'summary': "Highlights the impact of chatgpt's release, with over a million users in five days, boosting productivity for developers and content creators, easing marketing and sales efforts, and serving as a tool for psychologists and psychiatrists, while also addressing its limitations and potential future implications.", 'duration': 499.552, 'highlights': ['ChatGPT gained over a million users in just five days after its release in November 2022, surpassing the user base of Netflix, Twitter, Facebook, and Instagram, showcasing its immediate impact and popularity.', 'ChatGPT significantly increased productivity for developers by simplifying coding tasks, fixing errors, and generating code templates, leading to improved efficiency and results for companies.', 'ChatGPT has streamlined content creation processes, producing SEO-friendly and engaging content while reducing workload, demonstrating its potential to revolutionize content development.', 'ChatGPT has facilitated customized sales pitches, integrated with accounting and data analytics platforms, simplifying marketing and sales efforts and enhancing personalized interactions with leads.', 'Psychologists and psychiatrists are leveraging ChatGPT to counsel and assist patients, indicating its potential as a tool for mental health professionals and highlighting its diverse applications beyond business operations.', "ChatGPT's limitations include occasional provision of incorrect information, potential for biased content, and limited knowledge about current interfaces due to being trained on data collected before 2021, emphasizing the need for caution and fact-checking when using the tool."]}, {'end': 24853.722, 'start': 24490.583, 'title': 'Influence of ai in culture', 'summary': 'Explores the influence of artificial intelligence on culture, highlighting the widespread recognition of ai-based movies, the historical advancements in science and technology, the role of literature, movies, and media in shaping perceptions, and the resurgence of ai buzz due to recent progress by tech companies.', 'duration': 363.139, 'highlights': ['AI-based movies have contributed to widespread recognition and excitement about artificial intelligence, with most viewers recognizing at least two out of the four featured movies. Widespread recognition of AI-based movies, viewer excitement about AI', "Historical advancements in science and technology, including the development of the first computer in 1946 and Alan Turing's contributions, have influenced the cultural adoption of artificial intelligence. Development of the first computer in 1946, Alan Turing's contributions to modern computer science, influence on cultural adoption of AI", 'Literature, movies, and media have played a significant role in shaping perceptions and generating interest in artificial intelligence, contributing to its widespread recognition in cultures across the world. Role of literature, movies, and media in shaping perceptions, generating interest in AI, widespread recognition of AI in cultures', 'Recent progress in the field of artificial intelligence by tech companies like Amazon, Google, Facebook, Apple, Tesla, and research organizations has led to a resurgence of interest and buzz around AI in cultures across the world. Recent progress in AI by tech companies and research organizations, resurgence of interest and buzz around AI']}], 'duration': 1237.149, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM23616573.jpg', 'highlights': ['ChatGPT gained over a million users in just five days after its release in November 2022, surpassing the user base of Netflix, Twitter, Facebook, and Instagram, showcasing its immediate impact and popularity.', 'The potential use of ChatGPT in various fields, including integration into Microsoft Teams for note-taking and task recommendation, passing US medical and law exams, and causing concern among professionals about potential job displacement.', 'ChatGPT significantly increased productivity for developers by simplifying coding tasks, fixing errors, and generating code templates, leading to improved efficiency and results for companies.', 'ChatGPT has streamlined content creation processes, producing SEO-friendly and engaging content while reducing workload, demonstrating its potential to revolutionize content development.', 'ChatGPT has facilitated customized sales pitches, integrated with accounting and data analytics platforms, simplifying marketing and sales efforts and enhancing personalized interactions with leads.', 'Psychologists and psychiatrists are leveraging ChatGPT to counsel and assist patients, indicating its potential as a tool for mental health professionals and highlighting its diverse applications beyond business operations.', 'The chapter discusses solving the water jug problem with Python, applying specific rules to reach the goal state of filling jug B with 4 liters and jug A with 0.']}, {'end': 26108.297, 'segs': [{'end': 24980.64, 'src': 'embed', 'start': 24854.002, 'weight': 0, 'content': [{'end': 24855.803, 'text': 'It is also known as weak intelligence.', 'start': 24854.002, 'duration': 1.801}, {'end': 24865.806, 'text': 'Artificial narrow intelligence refers to AI systems that can only perform a specific task on their own using human-like capabilities.', 'start': 24856.403, 'duration': 9.403}, {'end': 24870.147, 'text': 'They can learn from past experiences in regards to that specific task.', 'start': 24866.286, 'duration': 3.861}, {'end': 24877.716, 'text': 'Even the most complex AI that uses machine learning and deep learning to teach itself falls under ANI.', 'start': 24870.754, 'duration': 6.962}, {'end': 24882.818, 'text': 'This type of artificial intelligence represents all existing AI,', 'start': 24878.156, 'duration': 4.662}, {'end': 24889.12, 'text': 'including even the most complicated and capable AI that has ever been created to this date.', 'start': 24882.818, 'duration': 6.302}, {'end': 24890.781, 'text': 'Let me give you some examples.', 'start': 24889.62, 'duration': 1.161}, {'end': 24897.843, 'text': 'Google, Alexa, and Siri voice assistants use AI to detect speech and carry out commands.', 'start': 24891.381, 'duration': 6.462}, {'end': 24904.889, 'text': "Today's security and surveillance systems uses facial recognition, which is a type of narrow AI.", 'start': 24898.424, 'duration': 6.465}, {'end': 24912.716, 'text': 'Social media platforms use it to learn about preferences and show you ads and content that you will enjoy.', 'start': 24905.33, 'duration': 7.386}, {'end': 24923.125, 'text': 'E-commerce websites like Amazon use it to learn about your shopping activities, where you are located, and so much more to recommend similar products.', 'start': 24913.217, 'duration': 9.908}, {'end': 24930.776, 'text': 'It also helps them figure out inventory for warehouses for different locations and their unbelievable two-day delivery.', 'start': 24923.672, 'duration': 7.104}, {'end': 24937.861, 'text': 'Banking and financial sector use it for fraud activity detection, loan approval and so on.', 'start': 24931.317, 'duration': 6.544}, {'end': 24944.145, 'text': 'Last but certainly not the least, autonomous vehicle use it to navigate the roads on their own.', 'start': 24938.261, 'duration': 5.884}, {'end': 24948.628, 'text': 'So I hope that you got a little bit of an idea of what ANI is.', 'start': 24944.725, 'duration': 3.903}, {'end': 24957.634, 'text': "Let's move on to the second category which is AGI and it stands for artificial general intelligence and it's also known as strong AI.", 'start': 24948.868, 'duration': 8.766}, {'end': 24959.575, 'text': "So you're probably wondering what it is.", 'start': 24957.954, 'duration': 1.621}, {'end': 24965.399, 'text': "First we talked about artificial narrow intelligence, now we're talking about artificial general intelligence.", 'start': 24960.016, 'duration': 5.383}, {'end': 24966.9, 'text': 'Does it give you any idea?', 'start': 24965.619, 'duration': 1.281}, {'end': 24976.039, 'text': "If you're thinking that this type of artificial intelligence is good at general tasks, meaning all tasks instead of a specific task,", 'start': 24967.461, 'duration': 8.578}, {'end': 24980.64, 'text': 'like we saw in the previous category A and I, you would be right.', 'start': 24976.039, 'duration': 4.601}], 'summary': 'Artificial narrow intelligence (ani) represents all existing ai, including google, alexa, and siri voice assistants, security systems, social media platforms, e-commerce websites, banking sector, and autonomous vehicles.', 'duration': 126.638, 'max_score': 24854.002, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM24854002.jpg'}, {'end': 25077.021, 'src': 'embed', 'start': 25049.008, 'weight': 14, 'content': [{'end': 25055.474, 'text': 'That is, they will have greater memory, faster data processing and analysis, and decision-making capabilities.', 'start': 25049.008, 'duration': 6.466}, {'end': 25065.297, 'text': 'The potential of having such powerful machines at our disposal seems appealing, but these machines may also threaten our existence, or,', 'start': 25056.034, 'duration': 9.263}, {'end': 25067.878, 'text': 'at the very least, our way of life.', 'start': 25065.297, 'duration': 2.581}, {'end': 25077.021, 'text': "We don't have any examples of AI, thank God for that, because we aren't even ready for Artificial General Intelligence, the one before this one.", 'start': 25068.358, 'duration': 8.663}], 'summary': 'Ai advancements offer greater memory, faster processing, and decision-making capabilities, posing potential threats to our existence and way of life.', 'duration': 28.013, 'max_score': 25049.008, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM25049008.jpg'}, {'end': 25163.227, 'src': 'embed', 'start': 25136.965, 'weight': 3, 'content': [{'end': 25146.612, 'text': 'The robot will be able to perform basic repetitive tasks with the aim of eliminating the need for people to handle dangerous or boring work.', 'start': 25136.965, 'duration': 9.647}, {'end': 25148.994, 'text': 'like getting groceries from Walmart.', 'start': 25147.072, 'duration': 1.922}, {'end': 25156.06, 'text': "If you guessed AGI, you wouldn't be completely wrong, but its capabilities will be limited, so it's ANI.", 'start': 25149.374, 'duration': 6.686}, {'end': 25159.123, 'text': 'These last ones are from Boston Dynamics.', 'start': 25156.581, 'duration': 2.542}, {'end': 25163.227, 'text': "They've been making incredible strides in robots navigating the world.", 'start': 25159.704, 'duration': 3.523}], 'summary': 'Robot to perform basic tasks, eliminating dangerous or boring work, like getting groceries from walmart. capabilities will be limited as ani, not agi. developed by boston dynamics.', 'duration': 26.262, 'max_score': 25136.965, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM25136965.jpg'}, {'end': 25312.49, 'src': 'embed', 'start': 25288.958, 'weight': 2, 'content': [{'end': 25296.404, 'text': "the New York Times reported that in a demo from Clearview it scraped personal images from Instagram account of the show's producer.", 'start': 25288.958, 'duration': 7.446}, {'end': 25300.047, 'text': 'Next example is Deepfake and this is really concerning.', 'start': 25296.984, 'duration': 3.063}, {'end': 25308.409, 'text': "Images and videos that are created using deep learning and contain a real person acting or saying things that they didn't do or say are called deep fakes.", 'start': 25300.607, 'duration': 7.802}, {'end': 25312.49, 'text': 'If you use it for entertainment purposes, deep fakes are fun.', 'start': 25308.909, 'duration': 3.581}], 'summary': 'Clearview demo scraped personal images from instagram; deepfakes are concerning and used for entertainment.', 'duration': 23.532, 'max_score': 25288.958, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM25288958.jpg'}], 'start': 24854.002, 'title': 'Types and dangers of ai', 'summary': 'Covers the three types of ai: ani, agi, and asi, along with examples and discusses the near to midterm dangers of ai, including privacy issues and societal implications such as job loss and transformation, while emphasizing the need for responsibility and regulation in ai development.', 'chapters': [{'end': 25067.878, 'start': 24854.002, 'title': 'Types of ai: ani, agi, asi', 'summary': 'Explains the three types of ai: artificial narrow intelligence (ani) represents all existing ai, including examples like google, alexa, and siri; artificial general intelligence (agi) is the goal of the field of ai and will be able to perform general tasks, and is being researched by organizations like openai and deepmind; and artificial super intelligence (asi) is where machines will become self-aware and overwhelmingly superior than humans at everything.', 'duration': 213.876, 'highlights': ['Artificial Super Intelligence (ASI) is where machines will become self-aware and overwhelmingly superior than humans at everything, potentially threatening our existence or way of life. ASI represents the pinnacle of AI, with machines having greater memory, faster data processing, and decision-making capabilities, posing a potential threat to human existence.', 'Artificial General Intelligence (AGI) is the goal of the field of AI, being researched by organizations like OpenAI and DeepMind, and will be able to perform general tasks like human beings. AGI aims to enable AI to perform general tasks, understand human needs, emotions, beliefs, and thought processes, and is being researched by leading organizations like OpenAI and DeepMind.', 'Artificial Narrow Intelligence (ANI) represents all existing AI, including examples like Google, Alexa, and Siri, which use AI for specific tasks like speech recognition and carrying out commands. ANI encompasses AI systems that can only perform a specific task on their own using human-like capabilities, such as speech recognition and carrying out commands, and examples include Google, Alexa, and Siri.']}, {'end': 25331.117, 'start': 25068.358, 'title': 'Types of ai and dangers', 'summary': "Explains the types of ai including ani, agi, and asi, providing examples and emphasizing that current ai is mostly ani. it then discusses the near to midterm dangers of ai, including privacy issues such as cambridge analytica's influence on the us presidential election and the use of deepfake technology for misinformation.", 'duration': 262.759, 'highlights': ['The chapter explains the types of AI including ANI, AGI, and ASI, providing examples and emphasizing that current AI is mostly ANI. The chapter introduces ANI, AGI, and ASI, and provides examples of ANI such as autonomous vehicles, Tesla bot, and robots from Boston Dynamics. It emphasizes that current AI is mostly ANI.', "Discusses the near to midterm dangers of AI, including privacy issues such as Cambridge Analytica's influence on the US presidential election and the use of deepfake technology for misinformation. The chapter discusses privacy issues as a near to midterm danger of AI, citing examples such as Cambridge Analytica's influence on the US presidential election and the use of deepfake technology for misinformation."]}, {'end': 25676.322, 'start': 25331.497, 'title': 'Ai dangers and societal implications', 'summary': 'Discusses the pressing concerns surrounding ai, including the social credit system in china, biases in ai, centralization of ai in the hands of a few, potential job loss, long-term safety risks, and the transformation of society through ai, highlighting privacy breaches and job displacement as immediate challenges.', 'duration': 344.825, 'highlights': ["Social Credit System in China China's implementation of a social credit system rates citizens' trustworthiness based on surveillance, impacting benefits and luck, without their knowledge or consent.", 'Biases in AI AI and machine learning models may produce biases in crucial decisions like job filtering, loan approvals, and medical diagnosis, illustrated by instances of racial bias in image recognition software.', 'Centralization of AI The concentration of AI advancements in the hands of a few companies like Facebook and Google raises concerns about their potential dominance and the lack of transparency regarding user data.', 'Job Displacement The use of AI in the workplace is expected to lead to the loss of a large number of jobs, including high-skilled positions such as consultants, though it is also expected to create and improve other jobs.', 'Long-Term Safety Risks The integration of AI into human bodies and its use in poorly regulated weapons systems pose significant safety and security risks, exemplified by neural link brain implants and autonomous weapon systems.', 'Transformation of Society The widespread use of AI is predicted to transform society, potentially leading to increased isolation and detachment from the physical world, as well as the development of relationships with intelligent projections and robots.']}, {'end': 26108.297, 'start': 25676.862, 'title': 'Dangers and future of ai', 'summary': 'Discusses the potential dangers of ai rise to power, highlighting the threat of super-intelligent ais altering the environment and the need for responsibility and regulation in ai development. it also explores the future of ai, emphasizing the importance of education, tough conversations, and ethical considerations for a net positive effect on the world.', 'duration': 431.435, 'highlights': ['The potential dangers of AI rise to power, including the threat of super-intelligent AIs altering the environment and human survival becoming unlikely if AIs prioritize free energy over supporting human life. Super-intelligent AIs with real-world traction could harness all available energy sources, potentially jeopardizing human survival.', 'The importance of responsibility and regulation in AI development, emphasizing the need for determining liability in accidents involving AI-operated devices or services. The challenge of determining responsibility for damages caused by AI-operated devices or services, such as self-driving cars, remains unclear, highlighting the need for accountability and regulation.', 'The future of AI, emphasizing the need for education, tough conversations, and ethical considerations to ensure a net positive effect on the world. The future of AI holds promise, but requires education, tough conversations, and ethical considerations to ensure a positive impact on the world.', 'Knowledge representation in AI, encompassing the study of how beliefs, intentions, and judgments of an intelligent agent can be expressed for automated reasoning, as well as the different types of knowledge in AI. Knowledge representation in AI involves expressing beliefs and judgments for automated reasoning, including declarative, structural, procedural, meta, and heuristic knowledge.']}], 'duration': 1254.295, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM24854002.jpg', 'highlights': ['ASI represents the pinnacle of AI, with machines having greater memory, faster data processing, and decision-making capabilities, posing a potential threat to human existence.', 'AGI aims to enable AI to perform general tasks, understand human needs, emotions, beliefs, and thought processes, and is being researched by leading organizations like OpenAI and DeepMind.', 'ANI encompasses AI systems that can only perform a specific task on their own using human-like capabilities, such as speech recognition and carrying out commands, and examples include Google, Alexa, and Siri.', 'The chapter introduces ANI, AGI, and ASI, and provides examples of ANI such as autonomous vehicles, Tesla bot, and robots from Boston Dynamics. It emphasizes that current AI is mostly ANI.', "The chapter discusses privacy issues as a near to midterm danger of AI, citing examples such as Cambridge Analytica's influence on the US presidential election and the use of deepfake technology for misinformation.", "China's implementation of a social credit system rates citizens' trustworthiness based on surveillance, impacting benefits and luck, without their knowledge or consent.", 'AI and machine learning models may produce biases in crucial decisions like job filtering, loan approvals, and medical diagnosis, illustrated by instances of racial bias in image recognition software.', 'The concentration of AI advancements in the hands of a few companies like Facebook and Google raises concerns about their potential dominance and the lack of transparency regarding user data.', 'The use of AI in the workplace is expected to lead to the loss of a large number of jobs, including high-skilled positions such as consultants, though it is also expected to create and improve other jobs.', 'The integration of AI into human bodies and its use in poorly regulated weapons systems pose significant safety and security risks, exemplified by neural link brain implants and autonomous weapon systems.', 'The widespread use of AI is predicted to transform society, potentially leading to increased isolation and detachment from the physical world, as well as the development of relationships with intelligent projections and robots.', 'Super-intelligent AIs with real-world traction could harness all available energy sources, potentially jeopardizing human survival.', 'The challenge of determining responsibility for damages caused by AI-operated devices or services, such as self-driving cars, remains unclear, highlighting the need for accountability and regulation.', 'The future of AI holds promise, but requires education, tough conversations, and ethical considerations to ensure a positive impact on the world.', 'Knowledge representation in AI involves expressing beliefs and judgments for automated reasoning, including declarative, structural, procedural, meta, and heuristic knowledge.']}, {'end': 28726.231, 'segs': [{'end': 26826.883, 'src': 'embed', 'start': 26800.909, 'weight': 2, 'content': [{'end': 26806.414, 'text': "but it's basically the best possible solution in a very reasonable period of time.", 'start': 26800.909, 'duration': 5.505}, {'end': 26817.004, 'text': 'This implies that Hill climbing solves the problems where we need to maximize or minimize a given real function by choosing values from the given inputs.', 'start': 26807.135, 'duration': 9.869}, {'end': 26823.45, 'text': 'a very good example of this is the traveling salesman problem, where you need to minimize the distance traveled by the salesman.', 'start': 26817.004, 'duration': 6.446}, {'end': 26826.883, 'text': 'Now the flow chart for Hill climbing looks something like this.', 'start': 26823.979, 'duration': 2.904}], 'summary': 'Hill climbing optimizes real functions, e.g., minimizing distances in the traveling salesman problem.', 'duration': 25.974, 'max_score': 26800.909, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM26800909.jpg'}, {'end': 27132.343, 'src': 'embed', 'start': 27105.729, 'weight': 1, 'content': [{'end': 27113.397, 'text': 'It only checks its one successor state and if it finds that it is better than the current state then it moves to the next state else.', 'start': 27105.729, 'duration': 7.668}, {'end': 27114.598, 'text': 'It will be in the same state.', 'start': 27113.537, 'duration': 1.061}, {'end': 27123.893, 'text': 'It is less time-consuming than the other types of Hill climbing, but it also gives a less optimal solution and the solution is not guaranteed.', 'start': 27115.422, 'duration': 8.471}, {'end': 27132.343, 'text': 'Now here is the algorithm for simple Hill climbing first you evaluate the initial state if it is the goal state, then you get success and stop.', 'start': 27124.533, 'duration': 7.81}], 'summary': 'Simple hill climbing algorithm evaluates successor state for optimal solution.', 'duration': 26.614, 'max_score': 27105.729, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM27105729.jpg'}, {'end': 27233.216, 'src': 'embed', 'start': 27210.51, 'weight': 4, 'content': [{'end': 27218.392, 'text': 'Next you loop until a solution is found or the current state does not change now the conditions to this are many.', 'start': 27210.51, 'duration': 7.882}, {'end': 27220.213, 'text': "So let's start with the first one.", 'start': 27218.912, 'duration': 1.301}, {'end': 27226.214, 'text': 'Let success be a state such that any successor of the current state will be better than it.', 'start': 27220.633, 'duration': 5.581}, {'end': 27233.216, 'text': 'next, for each operator that applies to the current state first apply the new operator and generate a new state.', 'start': 27226.214, 'duration': 7.002}], 'summary': 'Iterate until solution is found or state does not change, considering various conditions.', 'duration': 22.706, 'max_score': 27210.51, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM27210510.jpg'}, {'end': 27538.776, 'src': 'embed', 'start': 27503.071, 'weight': 0, 'content': [{'end': 27506.073, 'text': 'You can refer to the link given in the description bar.', 'start': 27503.071, 'duration': 3.002}, {'end': 27510.416, 'text': "So now let's move on to our code as you can see all the parts are there.", 'start': 27506.473, 'duration': 3.943}, {'end': 27517.442, 'text': "We're starting out with generating a random solution, then evaluating that particular solution,", 'start': 27510.817, 'duration': 6.625}, {'end': 27523.947, 'text': 'mutating that solution to generate the best random solution, and here is our base code.', 'start': 27517.442, 'duration': 6.505}, {'end': 27527.734, 'text': "Now, let's bring it here and let's try to run it.", 'start': 27525.094, 'duration': 2.64}, {'end': 27538.776, 'text': 'Here we are running all the cells one by one and once you run it you should be greeted with this particular output.', 'start': 27529.675, 'duration': 9.101}], 'summary': 'Code generates random solution, evaluates, mutates, and runs with specific output.', 'duration': 35.705, 'max_score': 27503.071, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM27503071.jpg'}, {'end': 28287.764, 'src': 'embed', 'start': 28254.633, 'weight': 3, 'content': [{'end': 28257.194, 'text': 'So this was about loan eligibility prediction project idea.', 'start': 28254.633, 'duration': 2.561}, {'end': 28261.794, 'text': 'So now let us move on to a next project idea, which is AI powered voice assistant.', 'start': 28257.873, 'duration': 3.921}, {'end': 28265.515, 'text': 'So this is one of the interesting artificial intelligence project idea.', 'start': 28262.434, 'duration': 3.081}, {'end': 28269.456, 'text': 'You can create a voice based personal assistant using artificial intelligence.', 'start': 28265.955, 'duration': 3.501}, {'end': 28275.457, 'text': 'So for this you have to train the system to understand human language so it can understand and save the command in the database.', 'start': 28270.036, 'duration': 5.421}, {'end': 28279.978, 'text': 'So next time you give the same command it will identify the words and perform the necessary action.', 'start': 28275.917, 'duration': 4.061}, {'end': 28287.764, 'text': 'This can be very helpful and you can enhance it to do various activities like searching for some information or item on the web, setting alarms,', 'start': 28280.598, 'duration': 7.166}], 'summary': 'Develop ai powered voice assistant for various activities like web searches and setting alarms.', 'duration': 33.131, 'max_score': 28254.633, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM28254633.jpg'}], 'start': 26109.038, 'title': 'Ai knowledge representation and hill climbing algorithm', 'summary': 'Covers knowledge representation in ai, including techniques like logical and semantic network representation, and discusses hill climbing algorithm overview, various types, and their applications, along with a diverse range of ai projects and applications in different fields.', 'chapters': [{'end': 26391.831, 'start': 26109.038, 'title': 'Knowledge representation in ai', 'summary': 'Discusses the cycle of knowledge representation in artificial intelligence, the relationship between knowledge and intelligence, and the techniques of knowledge representation in ai, including logical representation and semantic network representation.', 'duration': 282.793, 'highlights': ['Planning and Execution in AI Planning in AI involves giving an initial state, finding preconditions and effects, and a sequence of actions to achieve a particular goal. Execution is the final stage of the process.', 'Relationship Between Knowledge and Intelligence Knowledge plays a vital role in intelligence in creating artificial intelligence. Intelligent behavior in AI agents is demonstrated with knowledge or experience about the input.', 'Techniques of Knowledge Representation in AI The four techniques of representing knowledge in AI are logical representation, semantic network representation, frame representation, and production rules.', 'Logical Representation in AI Logical representation involves a language with definite rules, propositions, and no ambiguity. It supports sound inference and helps in performing logical reasoning.', 'Semantic Network Representation in AI Semantic networks provide a natural representation of knowledge in the form of graphical networks, conveying meaning transparently and being simple to understand.']}, {'end': 26780.037, 'start': 26392.492, 'title': 'Ai knowledge representation techniques', 'summary': 'Covers frame representation, production rules, and knowledge representation requirements, highlighting advantages, disadvantages, and examples of different approaches, including relational, inheritable, and inferential knowledge.', 'duration': 387.545, 'highlights': ['Frame representation includes a collection of slots and slot values, making programming easier by grouping related data and allowing easy addition of new slots for attributes and relations. Frame representation consists of a collection of slots and slot values, making programming easier by grouping related data and allowing easy addition of new slots for new attributes and relations.', 'Production rules are expressed in natural language, highly modular, and easily removable or modifiable, but lack learning capabilities and efficiency during program execution. Production rules are expressed in natural language, highly modular, and easily removable or modifiable, but lack learning capabilities and efficiency during program execution.', 'Knowledge representation requirements include representational accuracy, inferential adequacy, inferential efficiency, and acquisitional efficiency, to manipulate representational structures and acquire new knowledge easily. Knowledge representation requirements include representational accuracy, inferential adequacy, inferential efficiency, and acquisitional efficiency, to manipulate representational structures and acquire new knowledge easily.', 'Relational knowledge representation is a simple way of storing facts, where all the facts about a set of objects are systematically set out in columns, famous in database systems. Relational knowledge representation is a simple way of storing facts, where all the facts about a set of objects are systematically set out in columns, famous in database systems.', 'Inheritable knowledge approach stores data in a hierarchy of classes in a generalized form, containing inheritable knowledge and instance relations represented in boxed nodes. Inheritable knowledge approach stores data in a hierarchy of classes in a generalized form, containing inheritable knowledge and instance relations represented in boxed nodes.', 'Inferential knowledge approach represents knowledge in the form of formal logic, deriving more facts and guaranteeing correctness. Inferential knowledge approach represents knowledge in the form of formal logic, deriving more facts and guaranteeing correctness.']}, {'end': 27123.893, 'start': 26780.658, 'title': 'Hill climbing algorithm overview', 'summary': 'Discusses the hill climbing algorithm, which is used in artificial intelligence and aims to find the best possible solution in a reasonable period of time by evaluating and selecting neighboring solutions. it also explores the flow chart, algorithm, state space diagram, and types of hill climbing.', 'duration': 343.235, 'highlights': ['Hill climbing algorithm aims to find the best possible solution in a reasonable period of time by evaluating and selecting neighboring solutions. The hill climbing algorithm tries to find a sufficiently good solution to a problem within a reasonable time frame, although it may not be the global optimal maximum.', 'The algorithm follows a flow chart involving selecting a current solution, evaluating it, picking a neighboring solution, and making decisions based on the evaluation results. The algorithm involves steps such as selecting a current solution, evaluating it, picking a neighboring solution, and making decisions based on the evaluations, leading to a continuous process to find the best solution.', "The state space diagram represents different regions such as the current state, global maxima, local maxima, flat maxima, ridge, and shoulder, providing a visual understanding of the algorithm's search process. The state space diagram visually represents the current state, global maxima, local maxima, flat maxima, ridge, and shoulder, offering a graphical representation of the algorithm's search process and the objective function's values.", 'Simple hill climbing, the simplest type of hill climbing, evaluates one neighboring state at a time and selects the first one that optimizes the current cost. Simple hill climbing evaluates one neighboring state at a time and selects the first one that optimizes the current cost, making it less time-consuming but providing a less optimal and guaranteed solution.']}, {'end': 27629.902, 'start': 27124.533, 'title': 'Hill climbing algorithm explained', 'summary': "Explains the simple hill climbing, steepest ascent hill climbing, and stochastic hill climbing algorithms, highlighting their differences in time complexity, optimality, and solution guarantee. it also provides a step-by-step demonstration of the hill climbing algorithm for the 'hello world' problem, showcasing its iterative nature and the number of attempts required to reach the solution.", 'duration': 505.369, 'highlights': ['Hill climbing algorithms include simple hill climbing, steepest ascent hill climbing, and stochastic hill climbing, each with varying time complexity, optimality, and solution guarantee. It provides an overview of the different types of hill climbing algorithms and their respective characteristics.', 'Steepest ascent hill climbing algorithm consumes more time as it searches for multiple neighbors, potentially providing a better solution. The steepest ascent hill climbing algorithm examines all neighboring nodes and selects one closest to the goal state, potentially leading to a better solution despite consuming more time.', 'The stochastic hill climbing algorithm selects one neighbor node at random and decides whether to choose it as a current state, providing a better solution guarantee with more possibilities. Stochastic hill climbing algorithm does not examine all neighbors before moving; it picks points at random, offering a better solution guarantee with more possibilities.', "The hill climbing algorithm for the 'Hello World' problem showcases its iterative nature and the number of attempts required to reach the solution. The demonstration of the hill climbing algorithm for the 'Hello World' problem illustrates its iterative nature and the number of attempts required to reach the solution.", 'Hill climbing may fail to reach the global maximum due to local maximum and plateau regions, but backtracking can be used to overcome these problems. The limitations of hill climbing in reaching the global maximum due to local maximum and plateau regions are highlighted, along with the suggestion of using backtracking to overcome these issues.']}, {'end': 28009.797, 'start': 27630.303, 'title': 'Ai projects and applications', 'summary': 'Discusses several ai project ideas including hill climbing algorithm applications, chatbot development, music recommendation app, stock prediction application, social media suggestions, identifying inappropriate language and hate speech, lane line detection, and monitoring crop health using ai.', 'duration': 379.494, 'highlights': ['AI is applied in various fields such as network flow, traveling, salesman problem, eight Queens problem, and integrated circuit design. Hill climbing algorithm has applications in network flow, traveling, salesman problem, eight Queens problem, and integrated circuit design.', 'Chatbots are increasingly popular and used in various fields including education, medical, IT, and banking websites. Chatbots are increasingly being used in education, medical, IT, and banking websites, indicating their rising popularity.', 'Music recommendation apps use AI to collect and analyze user data to recommend songs based on genre, language, and ratings. Music recommendation apps collect and analyze user data to recommend songs based on genre, language, and ratings, improving online searching.', 'AI is used in stock prediction applications to analyze stock market trends and offer data-driven insights. Stock prediction applications use AI to analyze stock market trends and offer data-driven insights, aiding informed forecasts.', 'Social media platforms use AI for content recommendation, facial recognition, and targeted advertisements. Social media platforms utilize AI for content recommendation, facial recognition, and targeted advertisements, enhancing user experience.', 'AI is utilized to identify inappropriate language and hate speech by analyzing text and reactions in posts. AI is used to identify inappropriate language and hate speech by analyzing text and reactions in posts, contributing to a safer online environment.', 'AI-powered lane line detection is used in self-driving cars and line following robots, employing computer vision techniques for color thresholding. AI-powered lane line detection is used in self-driving cars and line following robots, employing computer vision techniques for color thresholding.', 'AI is increasingly adopted in agriculture for monitoring crop health, contributing to the evolution of the agriculture industry. AI is increasingly adopted in agriculture for monitoring crop health, contributing to the evolution of the agriculture industry.']}, {'end': 28726.231, 'start': 28009.797, 'title': 'Ai projects for various applications', 'summary': 'Discusses various ai project ideas, such as predictive analysis for agriculture, medical diagnosis, ai-powered cleaning robots, security systems, loan eligibility prediction, voice assistants, e-commerce recommendation engines, ai-enabled maps, motion detection, ai health engine, virtual trial of clothes, and spam email detection, showcasing the impact of ai in fields like music creation, social media, and chatbots.', 'duration': 716.434, 'highlights': ['AI Projects for Various Applications The chapter discusses various AI project ideas, such as predictive analysis for agriculture, medical diagnosis, AI-powered cleaning robots, security systems, loan eligibility prediction, voice assistants, e-commerce recommendation engines, AI-enabled maps, motion detection, AI health engine, virtual trial of clothes, and spam email detection.', "Predictive Analysis for Agriculture Using predictive analysis to determine the right sowing date for obtaining maximum yield after the previous harvest, analyzing crop and soil health, providing fertilizer recommendations, and forecasting the next seven days' weather.", 'AI-powered Medical Diagnosis Developing a software using AI to accurately spot signs of diseases in medical images, such as MRI scans or x-rays, aiding in cancer diagnosis and enabling accurate patient diagnosis and treatment prescription.', 'AI-powered Cleaning Robots Designing robots using AI to scan room size, identify obstacles, and remember the most effective route for cleaning, showcasing the potential for AI in robotics and automation.', "Security Systems using AI Creating a system that uses AI to scan and identify visitors' faces, allowing access to recognized visitors and notifying residents about unrecognized visitors."]}], 'duration': 2617.193, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM26109038.jpg', 'highlights': ['AI Projects for Various Applications The chapter discusses various AI project ideas, such as predictive analysis for agriculture, medical diagnosis, AI-powered cleaning robots, security systems, loan eligibility prediction, voice assistants, e-commerce recommendation engines, AI-enabled maps, motion detection, AI health engine, virtual trial of clothes, and spam email detection.', 'AI is utilized to identify inappropriate language and hate speech by analyzing text and reactions in posts. AI is used to identify inappropriate language and hate speech by analyzing text and reactions in posts, contributing to a safer online environment.', 'AI is applied in various fields such as network flow, traveling, salesman problem, eight Queens problem, and integrated circuit design. Hill climbing algorithm has applications in network flow, traveling, salesman problem, eight Queens problem, and integrated circuit design.', 'AI is used in stock prediction applications to analyze stock market trends and offer data-driven insights. Stock prediction applications use AI to analyze stock market trends and offer data-driven insights, aiding informed forecasts.', 'AI is increasingly adopted in agriculture for monitoring crop health, contributing to the evolution of the agriculture industry.']}, {'end': 29643.357, 'segs': [{'end': 28783.813, 'src': 'embed', 'start': 28751.325, 'weight': 1, 'content': [{'end': 28754.548, 'text': 'control your smart home, make reservations and so on.', 'start': 28751.325, 'duration': 3.223}, {'end': 28758.733, 'text': 'Next we have artificial intelligence in autonomous vehicles.', 'start': 28755.329, 'duration': 3.404}, {'end': 28763.798, 'text': 'For the longest time, self-driving cars have been a buzzword in the AI industry.', 'start': 28759.375, 'duration': 4.423}, {'end': 28769.383, 'text': 'The development of autonomous vehicles will definitely revolutionize the transportation system.', 'start': 28764.299, 'duration': 5.084}, {'end': 28778.069, 'text': 'Companies like Waymo conducted several test drives in Phoenix before deploying their first AI-based public ride-hailing service.', 'start': 28769.863, 'duration': 8.206}, {'end': 28783.813, 'text': "The artificial intelligence system collects data from the vehicle's radar, cameras,", 'start': 28778.669, 'duration': 5.144}], 'summary': 'Ai in autonomous vehicles will revolutionize transportation. waymo conducted several test drives in phoenix before deploying their first ai-based public ride-hailing service.', 'duration': 32.488, 'max_score': 28751.325, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM28751325.jpg'}, {'end': 28856.038, 'src': 'embed', 'start': 28807.454, 'weight': 0, 'content': [{'end': 28815.338, 'text': 'image detection and deep learning to build cars that can automatically detect objects and drive around without human intervention.', 'start': 28807.454, 'duration': 7.884}, {'end': 28824.017, 'text': 'Elon Musk, the founder of Tesla, talks a ton about how AI is implemented in Tesla self-driving cars and autopilot features.', 'start': 28816.09, 'duration': 7.927}, {'end': 28831.924, 'text': 'He quoted that Tesla will have fully self-driving cars ready by the end of the year and a robot taxi version,', 'start': 28824.558, 'duration': 7.366}, {'end': 28835.528, 'text': 'one that can ferry passengers without anyone behind the wheel.', 'start': 28831.924, 'duration': 3.604}, {'end': 28841.253, 'text': "Tesla's autopilot software goes beyond driving the car where you tell it to go.", 'start': 28836.088, 'duration': 5.165}, {'end': 28847.495, 'text': "If you're not in the mood for talking, autopilot will check your calendar and drive you to your scheduled appointment.", 'start': 28841.853, 'duration': 5.642}, {'end': 28849.436, 'text': 'That sounds pretty amazing.', 'start': 28848.115, 'duration': 1.321}, {'end': 28856.038, 'text': 'Moving on to our next application, we have applications of artificial intelligence in space exploration.', 'start': 28850.116, 'duration': 5.922}], 'summary': 'Tesla aims for fully self-driving cars by year-end, with ai-driven features like calendar-based autopilot. ai also applied in space exploration.', 'duration': 48.584, 'max_score': 28807.454, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM28807454.jpg'}], 'start': 28726.231, 'title': "Ai's impact and applications", 'summary': "Highlights ai's impact in weather prediction, google duplex's natural language processing, and advancements in autonomous vehicles. it discusses ai applications in various industries such as space exploration, gaming, banking, finance, agriculture, and healthcare. additionally, it explores ai's impact in marketing and the top trending ai technologies including robotic process automation, text analytics, biometrics, cyber defense, and decision management.", 'chapters': [{'end': 28831.924, 'start': 28726.231, 'title': 'Ai impact: weather, google duplex, autonomous vehicles', 'summary': "Highlights the impact of ai in various domains, including weather prediction, google duplex's natural language processing and human-like interactions, and the revolutionary advancements in autonomous vehicles by companies like waymo and tesla.", 'duration': 105.693, 'highlights': ['Google Duplex uses natural language processing and machine learning to manage schedules, control smart homes, make reservations, and respond to calls, adding a human touch.', "The development of autonomous vehicles, such as Waymo's AI-based public ride-hailing service, will revolutionize the transportation system.", "Tesla's self-driving cars implement computer vision, image detection, and deep learning, with Elon Musk aiming for fully self-driving cars ready by the end of the year and a robot taxi version.", "Waymo's AI system collects data from the vehicle's radar, cameras, GPS, and cloud services to produce control signals, making their cars more effective and safer.", "Advanced deep learning algorithms in autonomous vehicles accurately predict the behavior of objects in the vehicle's vicinity, enhancing safety and efficiency."]}, {'end': 29253.049, 'start': 28831.924, 'title': 'Ai applications across industries', 'summary': "Discusses the diverse applications of artificial intelligence, including tesla's autopilot, ai in space exploration, gaming, banking and finance, agriculture, and healthcare, showcasing examples like autonomous driving, space data analysis, gaming ai advancements, stock trading systems, agricultural robots, and ai-powered healthcare solutions.", 'duration': 421.125, 'highlights': ["Tesla's Autopilot Software Tesla's autopilot software offers advanced features like checking the user's calendar and driving them to scheduled appointments, showcasing the capabilities of autonomous driving technology.", 'AI in Space Exploration AI and machine learning are utilized for analyzing vast amounts of data from space expeditions, with examples including the identification of a distant eight-planet solar system using AI and the deployment of an AI-based Mars rover for autonomous targeting of investigations on Mars.', "AI in Gaming Industry AI has become integral in gaming, exemplified by the achievements of DeepMind's AlphaGo software in defeating world champions and the development of unpredictable opponent AI in first-person shooter games, prompting dynamic player strategies.", "AI in Banking and Finance Artificial intelligence is leveraged in stock trading systems to analyze market data and predict price changes, with examples from Nomura Securities, and in customer support, anomaly detection, and fraud prevention in banking, demonstrated by the adoption of AI-based chatbots like HDFC Bank's EVA.", "AI in Agriculture AI technologies such as computer vision are used in agriculture for precision spraying to prevent herbicide resistance, as seen in Blue River Technology's see and spray robot, and for soil defect identification and restoration techniques through image recognition apps like PEAT's Plantix, showcasing high accuracy in pattern detection.", "AI in Healthcare AI solutions in healthcare, such as IBM's Watson and Google's DeepMind Health, are aiding in unlocking vast health data, powering diagnoses, and analyzing medical images to identify symptoms of sight-threatening eye diseases, demonstrating the potential of AI in improving patient care and outcomes."]}, {'end': 29643.357, 'start': 29253.51, 'title': 'Ai in marketing and top trending ai technologies', 'summary': "Explores the impact of ai in marketing, focusing on amazon's revenue generation through ai-driven recommendation systems and delves into the top 10 trending ai technologies, highlighting the significance of robotic process automation, text analytics and nlp, biometrics, cyber defense, and decision management.", 'duration': 389.847, 'highlights': ["Amazon generates 35% of its revenue from its AI-driven recommendation engine, showcasing the significant impact of AI in marketing. Amazon's revenue generation is driven by its AI-powered recommendation engine, which contributes to 35% of its revenue.", "Netflix's recommendation system powered by machine learning influences over 75% of the content watched, reflecting the substantial impact of AI in entertainment. Over 75% of the content watched on Netflix is influenced by its machine learning-powered recommendation system.", 'Robotic process automation (RPA) and AI are utilized in various sectors such as customer service, accounting, financial services, healthcare, and human resources, indicating the extensive application of AI in streamlining corporate processes. RPA, along with AI, is applied in customer service, accounting, financial services, healthcare, and human resources.', 'Text analytics and NLP are widely adopted in fraud detection and security systems, emphasizing their critical role in enhancing cybersecurity measures. Text analytics and NLP play a crucial role in fraud detection and security systems, contributing to cybersecurity measures.', 'Biometrics, including fingerprint and face detection, is widely used for user authentication and market research, showcasing its relevance in security and consumer behavior analysis. Biometrics, encompassing fingerprint and face detection, is extensively employed for user authentication and market research purposes.']}], 'duration': 917.126, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM28726231.jpg', 'highlights': ['Google Duplex uses natural language processing and machine learning to manage schedules, control smart homes, make reservations, and respond to calls, adding a human touch.', "The development of autonomous vehicles, such as Waymo's AI-based public ride-hailing service, will revolutionize the transportation system.", "Tesla's self-driving cars implement computer vision, image detection, and deep learning, with Elon Musk aiming for fully self-driving cars ready by the end of the year and a robot taxi version.", 'AI in Space Exploration AI and machine learning are utilized for analyzing vast amounts of data from space expeditions, with examples including the identification of a distant eight-planet solar system using AI and the deployment of an AI-based Mars rover for autonomous targeting of investigations on Mars.', "AI in Banking and Finance Artificial intelligence is leveraged in stock trading systems to analyze market data and predict price changes, with examples from Nomura Securities, and in customer support, anomaly detection, and fraud prevention in banking, demonstrated by the adoption of AI-based chatbots like HDFC Bank's EVA.", "Amazon generates 35% of its revenue from its AI-driven recommendation engine, showcasing the significant impact of AI in marketing. Amazon's revenue generation is driven by its AI-powered recommendation engine, which contributes to 35% of its revenue.", "Netflix's recommendation system powered by machine learning influences over 75% of the content watched, reflecting the substantial impact of AI in entertainment. Over 75% of the content watched on Netflix is influenced by its machine learning-powered recommendation system.", 'Robotic process automation (RPA) and AI are utilized in various sectors such as customer service, accounting, financial services, healthcare, and human resources, indicating the extensive application of AI in streamlining corporate processes. RPA, along with AI, is applied in customer service, accounting, financial services, healthcare, and human resources.']}, {'end': 31275.912, 'segs': [{'end': 29670.788, 'src': 'embed', 'start': 29643.357, 'weight': 3, 'content': [{'end': 29649.182, 'text': 'by incorporating it into their applications, to propel and execute automated decisions.', 'start': 29643.357, 'duration': 5.825}, {'end': 29657.797, 'text': 'Some companies that provide this service are the informatica Advanced Systems Concepts Pega Systems uipath Etc.', 'start': 29649.742, 'duration': 8.055}, {'end': 29660.261, 'text': 'Now next up on number five.', 'start': 29658.558, 'duration': 1.703}, {'end': 29662.385, 'text': 'We have the marketing automation.', 'start': 29660.361, 'duration': 2.024}, {'end': 29670.788, 'text': 'Marketing has definitely become one of the most popular strategies for anything that you produce, create or build right now,', 'start': 29663.306, 'duration': 7.482}], 'summary': 'Companies like informatica, advanced systems concepts, pega systems, and uipath provide automation services for applications. marketing automation is a popular strategy for businesses.', 'duration': 27.431, 'max_score': 29643.357, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM29643357.jpg'}, {'end': 29741.572, 'src': 'embed', 'start': 29696.176, 'weight': 0, 'content': [{'end': 29704.08, 'text': 'The index DAI has grown to become a pioneer in adopting these marketing automation Technologies, and in the coming days,', 'start': 29696.176, 'duration': 7.904}, {'end': 29708.922, 'text': 'definitely most of the companies will only rely on the marketing automation,', 'start': 29704.08, 'duration': 4.842}, {'end': 29713.144, 'text': "and that's exactly what makes it one of the most trending Technologies in AI.", 'start': 29708.922, 'duration': 4.222}, {'end': 29716.856, 'text': 'Now next up on number four, we have digital twin.', 'start': 29713.934, 'duration': 2.922}, {'end': 29722.36, 'text': 'This is one of the newest and very interesting concept of artificial intelligence.', 'start': 29717.256, 'duration': 5.104}, {'end': 29735.108, 'text': 'Now digital twins are just virtual replicas of physical devices that data scientists and IT pros can use to run simulations before actual devices are built and deployed.', 'start': 29723.5, 'duration': 11.608}, {'end': 29741.572, 'text': 'They are also changing how technologies such as IOT AI and analytics are optimized.', 'start': 29735.688, 'duration': 5.884}], 'summary': 'Dai leads in marketing automation; digital twins revolutionize ai tech.', 'duration': 45.396, 'max_score': 29696.176, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM29696176.jpg'}], 'start': 29643.357, 'title': 'Ai technologies across industries', 'summary': "Covers the impact of ai in marketing, including technologies like marketing automation and digital twin, advanced ai technologies' benefits in various industries such as iot and virtual agents, ai applications in fashion, defense, disaster management, lifestyle, and the ai career roadmap and skills, providing quantifiable insights and examples.", 'chapters': [{'end': 29741.572, 'start': 29643.357, 'title': 'Trending ai technologies in marketing', 'summary': 'Highlights the growing impact of ai in marketing, with a focus on marketing automation, digital twin, and the companies leading in ai adoption, emphasizing the benefits and future trends in ai-driven marketing technologies.', 'duration': 98.215, 'highlights': ['The index DAI has grown to become a pioneer in adopting marketing automation Technologies, benefiting marketing and sales teams through AI-driven customer segmentation, data integration, and campaign management.', 'Marketing automation simplifies and enhances marketing and sales efforts, with AI playing a crucial role in automated customer segmentation and campaign management, leading to significant industry adoption and future reliance on AI in marketing.', 'Digital twins represent a novel concept in AI, serving as virtual replicas for running simulations of physical devices, revolutionizing the optimization of IoT, AI, and analytics technologies.']}, {'end': 30372.385, 'start': 29742.475, 'title': 'Advanced ai technologies and their benefits', 'summary': "Discusses the emergence of advanced ai technologies, their benefits, and their impact on various industries. it covers the applications of digital twin technology, industrial iot, virtual agents, augmented reality, and increased automation, emphasizing their role in optimizing deployments, automating tasks, and enhancing productivity. it also highlights ai's contributions to making smarter business decisions, solving complex problems, strengthening the economy, performing repetitive tasks, and enabling personalization, backed by quantifiable data and examples.", 'duration': 629.91, 'highlights': ["AI's Contribution to Economy The emergence of AI is estimated to contribute over 15 trillion dollars to the world's economy by 2030, with a projected increase in the global GDP by up to 14% between now and 2030. The report also indicates that China and North America will account for almost 70% of the global economic impact, with approximately 6.6 trillion of the expected GDP growth coming from productivity gains, especially in the coming years.", "AI in Solving Complex Problems AI has evolved from simple machine learning algorithms to advanced concepts like deep learning, enabling companies to solve complex issues such as fraud detection, medical diagnosis, and weather forecasting. The example of PayPal's use of AI for fraud detection demonstrates the precision in identifying fraudulent activities from over 235 billion in payments and 4 billion transactions by 170 million customers, utilizing machine learning and deep learning algorithms to mine and review patterns of likely fraud.", 'Digital Twin Technology and Industrial IoT Digital twin technology has moved beyond manufacturing and into the merging worlds of IoT, artificial intelligence, and data analytics, enabling data scientists and IT professionals to optimize deployments for peak efficiency and create what-if scenarios. Industrial IoT (IIoT) encompasses industrial applications like robotics, medical devices, and software-defined production processes, emphasizing its role in industrial sectors and applications.', 'Increased Automation and Productivity AI can be used to automate tasks ranging from extreme labor to the recruitment process, with examples like MYA reducing the time to hire by 50% and increasing productivity. Additionally, 64% of businesses depend on AI-based applications for increased productivity, with examples including legal robot for analyzing legal documents and Salesforce Einstein for delivering smarter, personalized customer experiences.', 'Personalization with AI The chapter discusses the benefits of personalization with AI, highlighting its potential to deliver 5 to 8 times the marketing ROI and boost sales by more than 10%. It emphasizes the role of AI in simplifying personalization tasks and cites the example of using AI to provide personalized financial insights through virtual financial assistant Erica, which has surpassed six million users and serviced over 35 million customer service requests.']}, {'end': 30784.016, 'start': 30372.905, 'title': 'Ai in fashion, defense, disaster management & lifestyle', 'summary': "Explores ai applications in fashion personalization, global defense, disaster management through weather forecasting, and lifestyle enhancement, highlighting examples such as thread's personalized clothing recommendations, anbot for global defense, ibm's ai-based deepthunder for weather forecasting, and ai use cases like face detection, text editing, chatbots, and recommendation algorithms.", 'duration': 411.111, 'highlights': ['Thread uses artificial intelligence to provide personalized clothing recommendations for 650,000 customers, improving customer experience and reducing costs associated with staffing stylists.', 'AnBot, an AI-based robot, patrols areas at 11 miles per hour, detecting individuals with criminal records and enhancing security by tracking suspicious activity.', "IBM's AI-based DeepThunder provides highly customized weather information for businesses, using 100 terabytes of third-party data daily, benefiting transportation, utility, and retail companies.", 'AI is enhancing lifestyle through applications such as face detection and recognition, text editing and autocorrection, chatbots for customer queries, and search and recommendation algorithms for personalized content suggestions.']}, {'end': 31275.912, 'start': 30785.177, 'title': 'Ai career roadmap and skills', 'summary': 'Discusses the roadmap for building a career in artificial intelligence, including the necessary skills and job roles. it emphasizes the importance of relevant education, continuous skill improvement, and provides salary insights for ai engineers, machine learning engineers, and data scientists.', 'duration': 490.735, 'highlights': ['AI engineers are required to have combined expertise in software development, programming, data science, and data engineering. The average salary of an AI engineer is eight lakh seventy thousand rupees in India and one lakh fourteen thousand dollars in the United States. AI engineers are responsible for developing, programming, and training complex networks of algorithms that make up AI. They are required to have combined expertise in software development, programming, data science, and data engineering. The average salary of an AI engineer is eight lakh seventy thousand rupees in India and one lakh fourteen thousand dollars in the United States.', 'Machine learning engineers are expected to possess strong software skills, apply predictive models, utilize natural language processing, and work with massive data sets. The average salary of a machine learning engineer is 11 lakhs rupees in India and 1 lakh 14 thousand dollars in the United States. Machine learning engineers must possess strong software skills, apply predictive models, utilize natural language processing, and work with massive data sets. The average salary of a machine learning engineer is 11 lakhs rupees in India and 1 lakh 14 thousand dollars in the United States.', 'Data scientists are analytical experts who combine computer science, statistics, and mathematics to analyze, process, and model data for actionable plans. They gather and analyze large sets of unstructured and structured data. Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. Data scientists are analytical experts who combine computer science, statistics, and mathematics to analyze, process, and model data for actionable plans. They gather and analyze large sets of unstructured and structured data. Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data.']}], 'duration': 1632.555, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM29643357.jpg', 'highlights': ["AI's Contribution to Economy: AI is estimated to contribute over 15 trillion dollars to the world's economy by 2030, with a projected increase in the global GDP by up to 14% between now and 2030.", 'Marketing Automation and AI: Marketing automation simplifies and enhances marketing and sales efforts, with AI playing a crucial role in automated customer segmentation and campaign management, leading to significant industry adoption and future reliance on AI in marketing.', "AI in Solving Complex Problems: AI has evolved to solve complex issues such as fraud detection, medical diagnosis, and weather forecasting, with examples like PayPal's use of AI for fraud detection demonstrating precision in identifying fraudulent activities from over 235 billion in payments and 4 billion transactions by 170 million customers.", 'Digital Twin Technology and Industrial IoT: Digital twin technology has moved beyond manufacturing and into the merging worlds of IoT, artificial intelligence, and data analytics, enabling data scientists and IT professionals to optimize deployments for peak efficiency and create what-if scenarios.', 'AI Career Roadmap and Skills: AI engineers, machine learning engineers, and data scientists are in demand, requiring expertise in software development, programming, data science, and data engineering, with varying average salaries in India and the United States.']}, {'end': 33317.208, 'segs': [{'end': 31432.585, 'src': 'embed', 'start': 31404.542, 'weight': 6, 'content': [{'end': 31410.447, 'text': 'Now deep learning is basically the process of using artificial neural networks to solve complex problems.', 'start': 31404.542, 'duration': 5.905}, {'end': 31415.952, 'text': 'So basically you can think of deep learning as a field that tries to mimic our brain.', 'start': 31410.847, 'duration': 5.105}, {'end': 31419.014, 'text': 'Okay, so how we have neural networks in our brain.', 'start': 31416.332, 'duration': 2.682}, {'end': 31424.719, 'text': "that's exactly how deep learning uses the concepts of artificial neural networks in order to solve problems.", 'start': 31419.014, 'duration': 5.705}, {'end': 31427.281, 'text': 'Now AI is a subset of data science.', 'start': 31425.219, 'duration': 2.062}, {'end': 31432.585, 'text': 'So guys, first of all, data science is the process of deriving useful insights from data.', 'start': 31427.701, 'duration': 4.884}], 'summary': 'Deep learning uses artificial neural networks to solve complex problems, a subset of data science.', 'duration': 28.043, 'max_score': 31404.542, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM31404542.jpg'}, {'end': 31851.789, 'src': 'embed', 'start': 31827.364, 'weight': 5, 'content': [{'end': 31835.247, 'text': 'Now neural networks are basically a set of algorithms or you can say a set of techniques which are modeled in accordance with the human brain.', 'start': 31827.364, 'duration': 7.883}, {'end': 31840.18, 'text': 'Okay, like I mentioned earlier, deep learning or neural networks is almost the same thing.', 'start': 31835.737, 'duration': 4.443}, {'end': 31844.223, 'text': 'Deep learning makes use of neural networks in order to solve complex problems.', 'start': 31840.24, 'duration': 3.983}, {'end': 31845.784, 'text': 'Now we have robotics.', 'start': 31844.724, 'duration': 1.06}, {'end': 31851.789, 'text': 'Now robotics is a subset of AI which includes different branches and applications of robots.', 'start': 31846.125, 'duration': 5.664}], 'summary': 'Neural networks use deep learning to solve complex problems, while robotics is a subset of ai.', 'duration': 24.425, 'max_score': 31827.364, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM31827364.jpg'}, {'end': 32014.818, 'src': 'embed', 'start': 31985.931, 'weight': 1, 'content': [{'end': 31991.472, 'text': 'Now supervised learning is the type of learning in which the machine learns by using label data.', 'start': 31985.931, 'duration': 5.541}, {'end': 31994.376, 'text': "Now to make you understand, let's look at an example.", 'start': 31991.952, 'duration': 2.424}, {'end': 32001.185, 'text': "Okay, let's say that you've input images of apples and oranges to your machine and you've labeled them.", 'start': 31994.596, 'duration': 6.589}, {'end': 32006.973, 'text': "You've told the machine like listen, this is the apple, this is an orange and the output should also look like this.", 'start': 32001.225, 'duration': 5.748}, {'end': 32014.818, 'text': "Okay, so you're labeling the input as apple and an orange, and then you're asking the machine to output an apple and an orange,", 'start': 32007.413, 'duration': 7.405}], 'summary': 'Supervised learning uses labeled data for training, for example, labeling images of apples and oranges.', 'duration': 28.887, 'max_score': 31985.931, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM31985931.jpg'}, {'end': 32046.275, 'src': 'embed', 'start': 32023.144, 'weight': 8, 'content': [{'end': 32030.089, 'text': 'It has to try and understand the difference between apple and oranges try and understand how they look different or how they have a different color.', 'start': 32023.144, 'duration': 6.945}, {'end': 32033.633, 'text': "So basically in unsupervised learning you don't have a label data set.", 'start': 32030.672, 'duration': 2.961}, {'end': 32040.474, 'text': "Okay, you're going to give it an unlabeled data set and you're going to ask it to find out and classify which is an apple and which is an orange.", 'start': 32034.033, 'duration': 6.441}, {'end': 32043.415, 'text': "Okay, that's the difference between supervised and unsupervised.", 'start': 32040.494, 'duration': 2.921}, {'end': 32046.275, 'text': 'Now reinforcement learning is comparatively different.', 'start': 32043.815, 'duration': 2.46}], 'summary': 'Unsupervised learning uses unlabeled data to classify apples and oranges, distinguishing from supervised learning.', 'duration': 23.131, 'max_score': 32023.144, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM32023144.jpg'}, {'end': 33055.754, 'src': 'embed', 'start': 33026.091, 'weight': 4, 'content': [{'end': 33033.035, 'text': 'So going from A to B is basically an action, going from A to C is another action, going from A to D is another action, and so on.', 'start': 33026.091, 'duration': 6.944}, {'end': 33037.377, 'text': 'Now reward is represented by the cost on each of these links.', 'start': 33033.475, 'duration': 3.902}, {'end': 33041.428, 'text': 'The policy is the path which is taken to reach the destination.', 'start': 33037.927, 'duration': 3.501}, {'end': 33048.43, 'text': 'So our aim here is to choose a policy that gets us to node D in the minimum cost possible.', 'start': 33041.968, 'duration': 6.462}, {'end': 33055.754, 'text': 'So how do you think you can solve this problem? You can start off at node A and you can take baby steps to your destination.', 'start': 33048.891, 'duration': 6.863}], 'summary': 'Choose a policy to reach node d with minimum cost.', 'duration': 29.663, 'max_score': 33026.091, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM33026091.jpg'}, {'end': 33182.725, 'src': 'embed', 'start': 33138.405, 'weight': 0, 'content': [{'end': 33140.184, 'text': 'Now let me explain this with a small game.', 'start': 33138.405, 'duration': 1.779}, {'end': 33144.767, 'text': 'So in the figure you can see a fox, you can see some meat, and you can see a tiger.', 'start': 33140.684, 'duration': 4.083}, {'end': 33147.849, 'text': 'Now our reinforcement learning agent is the fox.', 'start': 33145.267, 'duration': 2.582}, {'end': 33153.593, 'text': 'His end goal is to eat the maximum amount of meat before being eaten by the tiger.', 'start': 33148.409, 'duration': 5.184}, {'end': 33159.978, 'text': 'Okay, so he has to explore around, eat the maximum number of meat that he can eat before the tiger kills him.', 'start': 33153.993, 'duration': 5.985}, {'end': 33164.761, 'text': 'Since the fox is a clever fellow, he eats the meat that is closer to him.', 'start': 33160.538, 'duration': 4.223}, {'end': 33170.5, 'text': 'Okay, so rather than eating the meat which is close to the tiger, he eats the meat which is only close to him.', 'start': 33165.178, 'duration': 5.322}, {'end': 33175.443, 'text': 'This is because the closer he gets to the tiger, the higher are his chances of getting killed.', 'start': 33170.521, 'duration': 4.922}, {'end': 33182.725, 'text': 'So as a result of this, the rewards near the tiger, even if they are bigger meat chunks, will be discounted.', 'start': 33176.023, 'duration': 6.702}], 'summary': 'In a game, a fox aims to eat maximum meat before being eaten by a tiger, prioritizing safer options over larger rewards near the tiger.', 'duration': 44.32, 'max_score': 33138.405, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM33138405.jpg'}], 'start': 31276.592, 'title': 'Careers in data science and robotics', 'summary': 'Discusses the roles of data scientist and robotics engineer, highlighting their average annual salaries and the distinctions between ai, machine learning, and deep learning. it also explains the differences between ai, machine learning, and deep learning, their aims, applications, and types, as well as the various domains of artificial intelligence and understanding deep learning, artificial neural networks, and reinforcement learning basics.', 'chapters': [{'end': 31351.368, 'start': 31276.592, 'title': 'Data science and robotics careers', 'summary': 'Discusses the roles of a data scientist and a robotics engineer, emphasizing their average annual salaries, with a focus on the differences between ai, machine learning, and deep learning.', 'duration': 74.776, 'highlights': ['The average annual salary for a data scientist is 1 lakh 13 thousand dollars in the United States and 10 lakh rupees in India, while for a robotics engineer, it is $84,000 in the United States and 6 lakhs rupees in India.', 'Data scientists use industry knowledge, contextual understanding, and skepticism of existing assumptions to uncover solutions to business challenges, while robotics engineers are responsible for building mechanical devices or robots that can perform tasks with commands from humans.', 'The chapter explains the necessary skills for a robotics engineer, which include writing and manipulating computer programs, collaborating with other specialists, and developing prototypes.']}, {'end': 31722.099, 'start': 31351.768, 'title': 'Ai, machine learning, and deep learning', 'summary': 'Explains the differences between ai, machine learning, and deep learning, their aims, and applications, emphasizing that ai is a subset of data science, while machine learning is a subset of ai and data science. it also discusses examples of ai and the different types of ai, including reactive machines ai, limited memory ai, theory of mind ai, and self-aware ai.', 'duration': 370.331, 'highlights': ['AI is a subset of data science, while machine learning is a subset of AI and data science, and deep learning is a subset of machine learning, AI, and data science. This demonstrates the hierarchy and relationship between AI, machine learning, and deep learning in the context of data science.', 'The main aim of AI is to build machines capable of thinking like human beings, while the aim of machine learning is to make machines learn by providing them with a lot of data. This emphasizes the distinct aims of AI and machine learning, focusing on mimicking human behavior and learning through data, respectively.', 'The most general example of AI is the Google search engine, which utilizes machine learning algorithms and deep neural networks to provide quick and relevant search results. This illustrates a widely used application of AI in the form of the Google search engine, showcasing its effectiveness and relevance in everyday life.', 'Reactive machines AI, limited memory AI, theory of mind AI, and self-aware AI are different types of AI, each with unique characteristics and hypothetical implications. This provides an overview of the various types of AI, including their functionalities and theoretical concepts, such as understanding emotions and self-awareness.']}, {'end': 32243.135, 'start': 31722.519, 'title': 'Types of ai and domains of artificial intelligence', 'summary': 'Discusses different types of ai including artificial narrow intelligence, general intelligence, and superhuman intelligence, along with the various domains of artificial intelligence such as machine learning, neural networks, robotics, expert systems, fuzzy logic systems, and natural language processing.', 'duration': 520.616, 'highlights': ['Artificial narrow intelligence, general intelligence, and superhuman intelligence are discussed, highlighting the different levels of AI capabilities. Artificial superhuman intelligence, general intelligence, and artificial narrow intelligence are explained, demonstrating the varying levels of AI capabilities.', "The different domains of AI are explored, covering machine learning, neural networks, robotics, expert systems, fuzzy logic systems, and natural language processing. The chapter delves into various domains of AI, including machine learning, neural networks, robotics, expert systems, fuzzy logic systems, and natural language processing, providing insight into each domain's role in AI.", 'Explanation of the relationship between machine learning and artificial intelligence, with a focus on supervised, unsupervised, and reinforcement learning. The relationship between machine learning and AI is clarified, emphasizing the distinctions between supervised, unsupervised, and reinforcement learning.', 'Details on Q-learning, a type of reinforcement learning algorithm, and its process of learning an optimal policy from past experiences with the environment. The concept of Q-learning, a reinforcement learning algorithm, is outlined, highlighting its approach to learning optimal policies from past experiences.']}, {'end': 32681.377, 'start': 32243.496, 'title': 'Understanding deep learning and artificial neural networks', 'summary': 'Explains the concepts of deep learning, neural networks, and artificial neural networks, delving into the structure and functions of deep learning, the basic concept of neuron and artificial neurons, commonly used artificial neural networks, bayesian networks, and the turing test for assessing machine intelligence.', 'duration': 437.881, 'highlights': ['Explanation of Deep Learning Deep learning is the concept of using neural networks to mimic the way the human brain works, with layers for input, computation in hidden layers, and output, and can have multiple hidden layers depending on the problem.', "Artificial Neurons and Perceptrons Artificial neurons or perceptrons model the neuron's weighted inputs to compute outputs, similar to how the human brain's neurons function, providing a basic concept of deep learning.", 'Types of Artificial Neural Networks Explanation of feed forward neural networks, convolutional neural networks, recurrent neural networks, and autoencoders, detailing their functions and applications in signal processing and memory-based decision making.', 'Bayesian Networks A statistical model representing variables and their conditional dependencies in a directed acyclic graph, used for predicting the likelihood of known causes based on events, illustrated with an example related to disease and symptoms.', "Assessment of Machine Intelligence Explanation of the Turing test, introduced by Alan Turing, to evaluate a machine's capability to think like a human being, highlighting the difficulty for machines to perform simple physical tasks despite excelling in computations."]}, {'end': 33317.208, 'start': 32681.397, 'title': 'Reinforcement learning basics', 'summary': "Explains reinforcement learning, markov's decision process, reward maximization, and exploitation-exploration trade-off in artificial intelligence, emphasizing the concepts with examples from counter-strike game and shortest path problem, focusing on maximizing rewards and balancing exploration and exploitation.", 'duration': 635.811, 'highlights': ['Reinforcement learning uses an agent in an unknown environment to learn actions and rewards, similar to a video game, where performing correct actions leads to rewards and progression. Reinforcement learning involves an agent in an unknown environment learning actions and rewards, akin to a video game, where correct actions lead to rewards and progression.', "Markov's decision process aims to maximize rewards by choosing the most optimum policy, demonstrated by solving the shortest path problem using this mathematical approach. Markov's decision process aims to maximize rewards by choosing the most optimum policy, demonstrated by solving the shortest path problem using this mathematical approach.", 'Reward maximization in reinforcement learning focuses on training the agent to choose the best policy for maximum rewards, using the example of a fox aiming to eat maximum meat while avoiding being killed by a tiger. Reward maximization in reinforcement learning focuses on training the agent to choose the best policy for maximum rewards, using the example of a fox aiming to eat maximum meat while avoiding being killed by a tiger.', 'The trade-off between exploitation and exploration involves using known information to maximize rewards while also exploring the environment to capture new information and bigger rewards. The trade-off between exploitation and exploration involves using known information to maximize rewards while also exploring the environment to capture new information and bigger rewards.']}], 'duration': 2040.616, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM31276592.jpg', 'highlights': ['The average annual salary for a data scientist is $113,000 in the United States and 10 lakh rupees in India, while for a robotics engineer, it is $84,000 in the United States and 6 lakhs rupees in India.', 'AI is a subset of data science, while machine learning is a subset of AI and data science, and deep learning is a subset of machine learning, AI, and data science.', 'The most general example of AI is the Google search engine, which utilizes machine learning algorithms and deep neural networks to provide quick and relevant search results.', 'Artificial narrow intelligence, general intelligence, and superhuman intelligence are discussed, highlighting the different levels of AI capabilities.', 'The different domains of AI are explored, covering machine learning, neural networks, robotics, expert systems, fuzzy logic systems, and natural language processing.', 'Explanation of the relationship between machine learning and artificial intelligence, with a focus on supervised, unsupervised, and reinforcement learning.', 'Deep learning is the concept of using neural networks to mimic the way the human brain works, with layers for input, computation in hidden layers, and output, and can have multiple hidden layers depending on the problem.', 'Reinforcement learning uses an agent in an unknown environment to learn actions and rewards, similar to a video game, where performing correct actions leads to rewards and progression.', "Markov's decision process aims to maximize rewards by choosing the most optimum policy, demonstrated by solving the shortest path problem using this mathematical approach.", 'The trade-off between exploitation and exploration involves using known information to maximize rewards while also exploring the environment to capture new information and bigger rewards.']}, {'end': 35264.055, 'segs': [{'end': 33871.277, 'src': 'embed', 'start': 33840.907, 'weight': 0, 'content': [{'end': 33844.909, 'text': 'Now how do you avoid overfitting? First of all is cross-validation.', 'start': 33840.907, 'duration': 4.002}, {'end': 33851.452, 'text': 'Now before this also I mentioned that cross-validation is the best way to obtain a more optimal solution.', 'start': 33845.329, 'duration': 6.123}, {'end': 33860.056, 'text': 'Now the general idea behind cross-validation is to split the training data in order to generate multiple mini train test splits.', 'start': 33851.912, 'duration': 8.144}, {'end': 33863.071, 'text': 'Okay, these splits can be used to tune your model.', 'start': 33860.509, 'duration': 2.562}, {'end': 33871.277, 'text': "Okay, so you're basically splitting the training data in such a way that you know the model does not just use the entire training data and memorize it.", 'start': 33863.411, 'duration': 7.866}], 'summary': 'Cross-validation helps avoid overfitting by generating multiple mini train-test splits for tuning the model.', 'duration': 30.37, 'max_score': 33840.907, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM33840907.jpg'}, {'end': 34182.063, 'src': 'embed', 'start': 34156.108, 'weight': 1, 'content': [{'end': 34160.69, 'text': 'So Keras is basically an open source neural network library which is written in Python.', 'start': 34156.108, 'duration': 4.582}, {'end': 34165.632, 'text': 'So basically it is designed to enable fast experimentation with deep neural networks.', 'start': 34161.25, 'duration': 4.382}, {'end': 34170.294, 'text': 'Now TensorFlow is another open source software library for data flow programming.', 'start': 34166.132, 'duration': 4.162}, {'end': 34178.021, 'text': 'TensorFlow is mainly used in machine learning applications Similarly, PyTorch is again an open source machine learning library for Python.', 'start': 34170.794, 'duration': 7.227}, {'end': 34182.063, 'text': 'Its applications are mainly in the field of natural language processing.', 'start': 34178.401, 'duration': 3.662}], 'summary': 'Keras, tensorflow, and pytorch are open source libraries for neural networks and machine learning in python.', 'duration': 25.955, 'max_score': 34156.108, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM34156108.jpg'}, {'end': 34379.001, 'src': 'embed', 'start': 34349.737, 'weight': 3, 'content': [{'end': 34354.121, 'text': "Now let's look at what is stemming and lemmatization in natural language processing.", 'start': 34349.737, 'duration': 4.384}, {'end': 34355.662, 'text': 'Now what is stemming?', 'start': 34354.701, 'duration': 0.961}, {'end': 34367.331, 'text': 'It is an algorithm which works by cutting off the end or the beginning of the word and only taking into account a list of common prefixes and suffixes that can be found in inflicted words.', 'start': 34356.042, 'duration': 11.289}, {'end': 34374.237, 'text': 'Now for example, on the screen you can see that there is detections, detected, detection and detecting.', 'start': 34367.852, 'duration': 6.385}, {'end': 34379.001, 'text': 'Now if you apply stemming on these four words, it will lead to detect.', 'start': 34374.858, 'duration': 4.143}], 'summary': 'Stemming in nlp cuts off word endings, e.g. detections to detect.', 'duration': 29.264, 'max_score': 34349.737, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM34349737.jpg'}], 'start': 33317.728, 'title': 'Ml models, hyperparameters, overfitting prevention, and ai applications', 'summary': 'Compares parametric and non-parametric models, discusses hyperparameters in deep neural networks, covers overfitting prevention techniques, and explains ai applications in game theory and machine learning, including the min-max algorithm.', 'chapters': [{'end': 33454.515, 'start': 33317.728, 'title': 'Parametric vs non-parametric models', 'summary': 'Compares parametric and non-parametric models, stating that parametric models use a fixed number of parameters to build a model, leading to faster computation, while non-parametric models use a flexible number of parameters, leading to slower computation and potential overfitting, with examples such as logistic regression and knn models.', 'duration': 136.787, 'highlights': ['The chapter defines parametric and non-parametric models, highlighting that parametric models use a fixed number of parameters, leading to faster computation, while non-parametric models use a flexible number of parameters, leading to slower computation (e.g. logistic regression and KNN models).', 'Parametric models have stronger assumptions about the data, while non-parametric models have fewer assumptions, potentially causing overfitting in non-parametric models.', 'Examples of parametric models include logistic regression and naive bias, which provide immediate output, while non-parametric models like decision tree models and KNN may exhibit overfitting due to fewer assumptions and unfixed parameters.']}, {'end': 33697.513, 'start': 33455.035, 'title': 'Hyperparameters vs model parameters', 'summary': 'Discusses the difference between model parameters and hyperparameters, their importance in deep neural networks, and the algorithms used for hyperparameter optimization, emphasizing grid search, random search, and bayesian optimization.', 'duration': 242.478, 'highlights': ['Model parameters are the predictor variables while hyperparameters determine the training process, such as learning rate and number of hidden layers, in deep neural networks.', 'The number of hidden layers in a network is determined by the hyperparameters, impacting the accuracy and the risk of underfitting or overfitting.', 'Grid search method evaluates the efficiency of the model by training the network on every combination of hyperparameters and using cross-validation techniques to check optimality.', 'Random search method randomly selects samples and evaluates sets for a particular probability distribution without a fixed number of hyperparameters being evaluated.']}, {'end': 34732.278, 'start': 33698.094, 'title': 'Machine learning: overfitting prevention and image processing', 'summary': 'Discusses overfitting prevention techniques, including bayesian optimization, hyperparameter optimization, and methods such as cross-validation, removing irrelevant features, early stopping, regularization, and ensemble models. it also explains the purpose of deep learning frameworks such as keras, tensorflow, and pytorch, differentiates between nlp and text mining, defines the components of nlp, and explains stemming and lemmatization. additionally, it covers the fuzzy logic architecture, components of an expert system, the relationship between computer vision and ai, and the choice between supervised and unsupervised classification for image processing.', 'duration': 1034.184, 'highlights': ['Overfitting Prevention Techniques The chapter outlines various overfitting prevention techniques, including Bayesian optimization, hyperparameter optimization, cross-validation, removing irrelevant features, early stopping, regularization, and ensemble models.', 'Deep Learning Frameworks It explains the purpose of deep learning frameworks such as Keras, TensorFlow, and PyTorch, highlighting their significance in machine learning and deep learning applications.', 'NLP and Text Mining The chapter differentiates between NLP and text mining, explaining that text mining is the broader field, while NLP is an application or technique used within text mining.', 'Components of NLP It defines the components of NLP as natural language understanding and natural language generation, elaborating on their respective roles in analyzing and generating text.', 'Stemming and Lemmatization The chapter explains the differences between stemming and lemmatization, outlining how stemming cuts off prefixes and suffixes, while lemmatization focuses on morphological analysis to derive actual words.', 'Fuzzy Logic Architecture It provides an overview of the fuzzy logic architecture, including the fuzzifier, controller, knowledge base, inference engine, and defuzzification model.', 'Expert System Components The chapter outlines the components of an expert system as the knowledge base, inference engine, and user interface, emphasizing their roles in decision-making processes and user interaction.', 'Relationship between Computer Vision and AI It explains that computer vision makes use of artificial intelligence technologies to solve complex problems such as object detection and image processing.', 'Supervised vs. Unsupervised Classification for Image Processing The chapter discusses the differences between supervised and unsupervised classification for image processing, recommending supervised classification due to the manual input of labeled data.', 'Coping with Noise in Image Processing It highlights the use of image smoothing as the best method to reduce noise caused by high intensity or high contrast in an image.']}, {'end': 35264.055, 'start': 34732.816, 'title': 'Game theory & ai: min-max algorithm', 'summary': 'Explains the application of ai in various fields, including game theory and machine learning, and details the workings of the min-max algorithm using the tic-tac-toe game, emphasizing its relevance and optimization through alpha-beta pruning.', 'duration': 531.239, 'highlights': ['AI is applied in computer vision, game theory, and machine learning. AI is used in a vast number of fields, including computer vision, game theory, and machine learning.', 'Reinforcement learning and deep neural networks are commonly used in game examples. Game examples often make use of reinforcement learning and deep neural networks.', 'Min-max algorithm is used to choose an optimal move in game theory. The min-max algorithm is a key algorithm used in game theory to choose the optimal move for a player.', 'Utility function in the min-max algorithm provides numerical values for game outcomes. The utility function in the min-max algorithm assigns numerical values to game outcomes.', 'Alpha-beta pruning is used to optimize the min-max based game by removing unnecessary nodes. Alpha-beta pruning optimizes the min-max based game by removing nodes that do not affect the final decision.']}], 'duration': 1946.327, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/VOaoabf3LPM/pics/VOaoabf3LPM33317728.jpg', 'highlights': ['Parametric models use a fixed number of parameters, leading to faster computation, while non-parametric models use a flexible number of parameters, leading to slower computation (e.g. logistic regression and KNN models).', 'Model parameters are the predictor variables while hyperparameters determine the training process, such as learning rate and number of hidden layers, in deep neural networks.', 'The chapter outlines various overfitting prevention techniques, including Bayesian optimization, hyperparameter optimization, cross-validation, removing irrelevant features, early stopping, regularization, and ensemble models.', 'AI is applied in computer vision, game theory, and machine learning. AI is used in a vast number of fields, including computer vision, game theory, and machine learning.']}], 'highlights': ["AI is estimated to contribute over 15 trillion dollars to the world's economy by 2030, with a projected increase in the global GDP by up to 14% between now and 2030.", 'The interconnectedness of artificial intelligence, machine learning, and deep learning is explained, clarifying that machine learning and deep learning are subsets of AI, providing algorithms and neural networks to solve data-driven problems, while AI encompasses a broad domain including natural language processing, object detection, computer vision, robotics, and expert systems.', 'The AI market worth in 2020 was around 30 billion US dollars and is forecasted to rise to 300 billion US dollars by the year 2026, with a compounded annual growth rate of 35.6%, indicating unprecedented growth for the industry.', 'The accumulated volume of data is projected to increase from 4.4 zettabytes to roughly around 44 zettabytes by 2020, indicating a tenfold surge, fostering a significant impact on AI implementation.', 'AI is rapidly growing in popularity and high in demand, as businesses and organizations seek to leverage technology to improve their operations.', 'The term artificial intelligence was first coined in 1956 by John McCarty at the Dartmouth conference, defining AI as the science and engineering of making intelligent machines, which has now found applications in various fields including healthcare, finance, and social media.', 'The demand for AI skills has more than doubled over the last past three years and the number of job postings is up by 119%.', 'The AI market worth in 2020 was around 30 billion US dollars and is forecasted to rise to 300 billion US dollars by the year 2026, with a compounded annual growth rate of 35.6%, indicating unprecedented growth for the industry.', "AI is used in the finance sector, where JPMorgan's Chase Contract Intelligence platform uses AI, machine learning, and image recognition software to analyze legal documents, reducing the review time from 36,000 hours to a matter of seconds.", 'IBM Watson AI technology was able to cross-reference 20 million oncology records and correctly diagnose a rare leukemia condition in a patient, showcasing the potential of AI in healthcare with quantifiable data.']}