title
What is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutorial | Simplilearn

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🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=23AugustTubebuddyExpPCPAIandML&utm_medium=DescriptionFF&utm_source=youtube 🔥AI Engineer Masters Program (Discount Code - YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=SCE-AIMasters&utm_medium=DescriptionFF&utm_source=youtube 🔥AI & Machine Learning Bootcamp(US Only): https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=DeepLearning-FbxTVRfQFuI&utm_medium=Descriptionff&utm_source=youtube 🔥 Purdue Post Graduate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=DeepLearning-FbxTVRfQFuI&utm_medium=Descriptionff&utm_source=youtube This Deep Learning tutorial will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. Below topics are explained in this Deep Learning Tutorial: Start (0:00) 1. What is Deep Learning? ( 02:25 ) 2. Why do we need Deep Learning? ( 03:42 ) 3. Applications of Deep Learning ( 04:55 ) 4. What is Neural Network? ( 11:32 ) 5. Activation Functions ( 15:50 ) 6. Working of Neural Network ( 26:14 ) Check Out Simplilearn Deep Learning Course here: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=What-is-Deep-Learning-FbxTVRfQFuI&utm_medium=Tutorials&utm_source=youtube 📚What is DeepLearning Article: https://bit.ly/30cOWGa To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the Slide here: https://goo.gl/EX98sh Watch more videos on Deep Learning: https://www.youtube.com/playlist?list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip #DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse We've partnered with Purdue University and collaborated with IBM to offer you the unique Post Graduate Program in AI and Machine Learning. Learn more about it here - https://www.simplilearn.com/ai-and-machine-learning-post-graduate-certificate-program-purdue?utm_campaign=What-is-Deep-Learning-FbxTVRfQFuI&utm_medium=Tutorials&utm_source=youtube ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=What-is-Deep-Learning-FbxTVRfQFuI&utm_medium=Tutorials&utm_source=youtube 🔥Free Artificial Intelligence Course: https://www.simplilearn.com/learn-ai-basics-skillup?utm_campaign=DeepLearning&utm_medium=Description&utm_source=youtube 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

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{'title': 'What is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutorial | Simplilearn', 'heatmap': [{'end': 1968.702, 'start': 1934.389, 'weight': 1}], 'summary': 'Provides an introduction to deep learning, discussing its application in image recognition and various domains such as healthcare, robotics, and self-driving cars. it explains neural network basics, diverse applications including real-time language translation and music composition, activation functions, and the training process, aiming to achieve a threshold accuracy of 90 percent after tens to hundreds of iterations.', 'chapters': [{'end': 93.023, 'segs': [{'end': 74.386, 'src': 'embed', 'start': 30.056, 'weight': 0, 'content': [{'end': 31.737, 'text': 'It uses artificial neural network.', 'start': 30.056, 'duration': 1.681}, {'end': 38.464, 'text': 'It is trained with some known images and during the training it is told if it is recognizing correctly or not.', 'start': 32.037, 'duration': 6.427}, {'end': 43.949, 'text': 'And then when new images are submitted, it recognizes correctly based on the accuracy of course.', 'start': 38.764, 'duration': 5.185}, {'end': 48.23, 'text': 'So a little quick understanding about artificial neural networks.', 'start': 44.289, 'duration': 3.941}, {'end': 55.233, 'text': 'So this is the way it does is you provide a lot of training data, also known as labeled data.', 'start': 48.51, 'duration': 6.723}, {'end': 59.074, 'text': 'For example, in this case, these are the images of dogs.', 'start': 55.433, 'duration': 3.641}, {'end': 62.757, 'text': 'and the network extracts some features.', 'start': 59.794, 'duration': 2.963}, {'end': 65.358, 'text': 'that makes a dog a dog right.', 'start': 62.757, 'duration': 2.601}, {'end': 74.386, 'text': 'so that is known as feature extraction and based on that, when you submit a new image of dog, the basic features remain pretty much the same.', 'start': 65.358, 'duration': 9.028}], 'summary': 'Artificial neural network trained with labeled data recognizes images accurately', 'duration': 44.33, 'max_score': 30.056, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI30056.jpg'}], 'start': 3.151, 'title': 'Introduction to deep learning', 'summary': 'Introduces deep learning and discusses its application in image recognition through artificial neural networks, feature extraction, and training with labeled data.', 'chapters': [{'end': 93.023, 'start': 3.151, 'title': 'Introduction to deep learning', 'summary': 'Introduces deep learning, explaining its application in image recognition through artificial neural networks, feature extraction, and training with labeled data.', 'duration': 89.872, 'highlights': ['Artificial neural networks are used in deep learning for image recognition, where a machine is trained with labeled images to recognize objects such as cats or dogs based on features, achieving accuracy through training (e.g. recognizing correctly based on accuracy).', 'The process involves extracting features from labeled data, such as images of dogs, to enable the network to recognize common features that define the object, ensuring consistent recognition across different images of the same object (e.g. features of a dog remain similar across different images).', 'Deep learning utilizes labeled training data, where the network is trained with known images to enable accurate recognition of new images, demonstrating the importance of feature extraction for consistent recognition and the role of training in achieving accuracy.']}], 'duration': 89.872, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI3151.jpg', 'highlights': ['Artificial neural networks are used in deep learning for image recognition, achieving accuracy through training.', 'The process involves extracting features from labeled data to enable consistent recognition across different images.', 'Deep learning utilizes labeled training data to enable accurate recognition of new images.']}, {'end': 514.78, 'segs': [{'end': 211.579, 'src': 'embed', 'start': 179.784, 'weight': 0, 'content': [{'end': 182.705, 'text': 'And deep learning is a subset of machine learning.', 'start': 179.784, 'duration': 2.921}, {'end': 194.509, 'text': 'The primary difference between machine learning and deep learning is that deep learning uses neural networks and it is suitable for handling large amounts of unstructured data.', 'start': 182.865, 'duration': 11.644}, {'end': 200.772, 'text': 'And the last but not least, one of the major differences between machine learning and deep learning is that in machine learning,', 'start': 194.77, 'duration': 6.002}, {'end': 206.374, 'text': 'the feature extraction or the feature engineering is done by the data scientists manually.', 'start': 200.772, 'duration': 5.602}, {'end': 211.579, 'text': 'But in deep learning, since we use neural networks, the feature engineering happens automatically.', 'start': 206.594, 'duration': 4.985}], 'summary': 'Deep learning uses neural networks for automatic feature engineering and is suitable for handling large amounts of unstructured data.', 'duration': 31.795, 'max_score': 179.784, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI179784.jpg'}, {'end': 320.947, 'src': 'embed', 'start': 254.232, 'weight': 1, 'content': [{'end': 262.099, 'text': 'And increasingly for artificial intelligence, we need image recognition and we need to process, analyze images and voice.', 'start': 254.232, 'duration': 7.867}, {'end': 266.362, 'text': "That's the reason deep learning is required compared to, let's say, traditional machine learning.", 'start': 262.159, 'duration': 4.203}, {'end': 273.388, 'text': "it can also perform complex algorithms, more complex than, let's say, what machine learning can do,", 'start': 266.362, 'duration': 7.026}, {'end': 277.192, 'text': 'and it can achieve best performance with the large amounts of data.', 'start': 273.388, 'duration': 3.804}, {'end': 283.437, 'text': "so the more you have the data let's say, reference data or label data the better the system will do,", 'start': 277.192, 'duration': 6.245}, {'end': 286.54, 'text': 'because the training process will be that much better.', 'start': 283.437, 'duration': 3.103}, {'end': 292.705, 'text': 'and, last but not least, with deep learning you can really avoid the manual process of feature extraction.', 'start': 286.54, 'duration': 6.165}, {'end': 295.107, 'text': 'Those are some of the reasons why we need deep learning.', 'start': 292.805, 'duration': 2.302}, {'end': 297.188, 'text': 'Some of the applications of deep learning.', 'start': 295.227, 'duration': 1.961}, {'end': 305.054, 'text': 'Deep learning has made major inroads and it is a major area in which deep learning is applied is healthcare.', 'start': 297.268, 'duration': 7.786}, {'end': 311.879, 'text': 'And within healthcare, particularly oncology, which is basically cancer related stuff.', 'start': 305.374, 'duration': 6.505}, {'end': 320.947, 'text': 'One of the issues with cancer is that a lot of cancers today are curable, they can be cured, they are detected early on.', 'start': 312.279, 'duration': 8.668}], 'summary': 'Deep learning is essential for image & voice recognition, performing complex algorithms, achieving best performance with large data, and avoiding manual feature extraction. it is widely applied in healthcare, particularly in oncology for early cancer detection.', 'duration': 66.715, 'max_score': 254.232, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI254232.jpg'}, {'end': 454.591, 'src': 'embed', 'start': 425.56, 'weight': 4, 'content': [{'end': 430.645, 'text': 'Video is fed to them and they need to be able to detect objects, obstacles, and so on and so forth.', 'start': 425.56, 'duration': 5.085}, {'end': 432.325, 'text': "So that's where deep learning is used.", 'start': 430.965, 'duration': 1.36}, {'end': 436.386, 'text': 'They need to be able to hear and make sense of the sounds that they are hearing.', 'start': 432.365, 'duration': 4.021}, {'end': 438.607, 'text': 'That needs deep learning as well.', 'start': 436.586, 'duration': 2.021}, {'end': 442.528, 'text': 'So robotics is a major area where deep learning is applied.', 'start': 438.707, 'duration': 3.821}, {'end': 446.129, 'text': 'Then we have self-driving cars or autonomous cars.', 'start': 442.748, 'duration': 3.381}, {'end': 454.591, 'text': "You must have heard of Google's autonomous car which has been tested for millions of miles and pretty much incident free.", 'start': 446.409, 'duration': 8.182}], 'summary': 'Deep learning is crucial for robots to detect objects and understand sounds, especially in fields like robotics and autonomous cars.', 'duration': 29.031, 'max_score': 425.56, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI425560.jpg'}, {'end': 497.174, 'src': 'embed', 'start': 471, 'weight': 5, 'content': [{'end': 479.046, 'text': 'and it is predicted that in the next probably 10 to 15 years these will be in production and they will be used extensively in real life.', 'start': 471, 'duration': 8.046}, {'end': 483.649, 'text': 'Right now they are all in R&D and in test phases, but pretty soon these will be on the.', 'start': 479.266, 'duration': 4.383}, {'end': 486.87, 'text': 'So this is another area where deep learning is used.', 'start': 483.949, 'duration': 2.921}, {'end': 487.89, 'text': 'and how is it used?', 'start': 486.87, 'duration': 1.02}, {'end': 490.331, 'text': 'where is it used within autonomous driving?', 'start': 487.89, 'duration': 2.441}, {'end': 497.174, 'text': 'The car actually is fed with video of surroundings and it is supposed to process that information,', 'start': 490.451, 'duration': 6.723}], 'summary': 'In 10-15 years, deep learning will be extensively used in autonomous driving for processing video data.', 'duration': 26.174, 'max_score': 471, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI471000.jpg'}], 'start': 93.023, 'title': 'Deep learning applications', 'summary': "Explains artificial neural networks, difference between machine learning and deep learning, need for deep learning in handling unstructured data, and its applications in image recognition and complex algorithms. it also discusses deep learning's applications in healthcare for early cancer detection, in robotics for object detection and sound recognition, and in self-driving cars for obstacle detection and traffic signal recognition, with predictions of extensive use in the next 10 to 15 years.", 'chapters': [{'end': 297.188, 'start': 93.023, 'title': 'Understanding deep learning', 'summary': 'Explains the working of artificial neural networks, the difference between machine learning and deep learning, the need for deep learning in handling large amounts of unstructured data, and its applications in image recognition and complex algorithms.', 'duration': 204.165, 'highlights': ['Deep learning uses neural networks and is suitable for handling large amounts of unstructured data, enabling automatic feature engineering. The primary difference between machine learning and deep learning is that deep learning uses neural networks and it is suitable for handling large amounts of unstructured data. In deep learning, the feature engineering happens automatically.', 'Deep learning is required for image recognition, processing, and analyzing images and voice compared to traditional machine learning. Deep learning is required for image recognition and processing, analyzing images and voice, as traditional machine learning is not very good at handling large amounts of unstructured data.', 'Deep learning can perform complex algorithms and achieve best performance with large amounts of data, avoiding the manual process of feature extraction. Deep learning can perform complex algorithms, achieve best performance with large amounts of data, and avoid the manual process of feature extraction.']}, {'end': 514.78, 'start': 297.268, 'title': 'Applications of deep learning', 'summary': 'Discusses the application of deep learning in healthcare for early cancer detection, in robotics for object detection and sound recognition, and in self-driving cars for obstacle detection and traffic signal recognition, with predictions of extensive use in the next 10 to 15 years.', 'duration': 217.512, 'highlights': ['Deep learning is applied in healthcare for early cancer detection, expediting the screening process and automating the initial screening, with predictions of extensive use in the next 10 to 15 years. Early cancer detection, predictions of extensive use in the next 10 to 15 years', 'Deep learning is utilized in robotics for object detection, obstacle recognition, and sound interpretation, making it a major area of application. Object detection, obstacle recognition, sound interpretation', 'The application of deep learning in self-driving cars involves processing video feed to detect obstacles, recognize traffic signals, and ensure safe driving, with predictions of extensive use in the next 10 to 15 years. Obstacle detection, traffic signal recognition, predictions of extensive use in the next 10 to 15 years']}], 'duration': 421.757, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI93023.jpg', 'highlights': ['Deep learning uses neural networks and is suitable for handling large amounts of unstructured data, enabling automatic feature engineering.', 'Deep learning is required for image recognition, processing, and analyzing images and voice compared to traditional machine learning.', 'Deep learning can perform complex algorithms and achieve best performance with large amounts of data, avoiding the manual process of feature extraction.', 'Deep learning is applied in healthcare for early cancer detection, expediting the screening process and automating the initial screening, with predictions of extensive use in the next 10 to 15 years.', 'Deep learning is utilized in robotics for object detection, obstacle recognition, and sound interpretation, making it a major area of application.', 'The application of deep learning in self-driving cars involves processing video feed to detect obstacles, recognize traffic signals, and ensure safe driving, with predictions of extensive use in the next 10 to 15 years.']}, {'end': 1117.097, 'segs': [{'end': 581.282, 'src': 'embed', 'start': 555.004, 'weight': 0, 'content': [{'end': 563.651, 'text': 'There are probably at least hundreds of languages or if not more to translate each and every document into every language is pretty difficult.', 'start': 555.004, 'duration': 8.647}, {'end': 571.196, 'text': 'Therefore, we can use deep learning to do pretty much like a real-time translation mechanism.', 'start': 563.931, 'duration': 7.265}, {'end': 573.938, 'text': "So we don't have to translate everything and keep it ready.", 'start': 571.256, 'duration': 2.682}, {'end': 581.282, 'text': 'But we train applications or artificial intelligence systems that will do the translation on the fly.', 'start': 574.078, 'duration': 7.204}], 'summary': 'Using deep learning for real-time translation, reducing need for manual translation of hundreds of languages.', 'duration': 26.278, 'max_score': 555.004, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI555004.jpg'}, {'end': 680.181, 'src': 'embed', 'start': 640.153, 'weight': 1, 'content': [{'end': 643.435, 'text': 'so mission translation is another major area where deep learning is used.', 'start': 640.153, 'duration': 3.282}, {'end': 649.159, 'text': 'Then there are a few other upcoming areas where synthesizing is done by neural nets.', 'start': 643.615, 'duration': 5.544}, {'end': 652.542, 'text': 'For example, music composition and generation of music.', 'start': 649.299, 'duration': 3.243}, {'end': 657.986, 'text': 'So you can train a neural net to produce music, even to compose music.', 'start': 652.602, 'duration': 5.384}, {'end': 659.567, 'text': 'So this is a fun thing.', 'start': 658.266, 'duration': 1.301}, {'end': 660.928, 'text': 'This is still upcoming.', 'start': 659.847, 'duration': 1.081}, {'end': 664.311, 'text': 'It needs a lot of effort to train such neural net.', 'start': 661.409, 'duration': 2.902}, {'end': 666.192, 'text': 'It has been proved that it is possible.', 'start': 664.631, 'duration': 1.561}, {'end': 671.195, 'text': 'So this is a relatively new area and on the same lines, colorization of images.', 'start': 666.352, 'duration': 4.843}, {'end': 676.259, 'text': 'So these two images on the left hand side is a grayscale image or a black and white image.', 'start': 671.295, 'duration': 4.964}, {'end': 680.181, 'text': 'This was colored by a neural net or a deep learning application.', 'start': 676.339, 'duration': 3.842}], 'summary': 'Deep learning used in music composition and image colorization, upcoming and requires effort to train neural nets.', 'duration': 40.028, 'max_score': 640.153, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI640153.jpg'}, {'end': 842.892, 'src': 'embed', 'start': 791.008, 'weight': 4, 'content': [{'end': 792.809, 'text': 'This is the structure of a biological neuron.', 'start': 791.008, 'duration': 1.801}, {'end': 796.851, 'text': 'An artificial neural network is based on the human brain.', 'start': 793.009, 'duration': 3.842}, {'end': 803.415, 'text': 'The smallest component of artificial neural network is an artificial neuron as shown here.', 'start': 797.151, 'duration': 6.264}, {'end': 806.677, 'text': "Sometimes it's also referred to as a perceptron.", 'start': 803.635, 'duration': 3.042}, {'end': 808.758, 'text': 'now this is a very high level diagram.', 'start': 806.917, 'duration': 1.841}, {'end': 813.74, 'text': 'the artificial neuron has a small central unit which will receive the input.', 'start': 808.758, 'duration': 4.982}, {'end': 823.986, 'text': "if it is doing, let's say, image processing, the inputs could be pixel values of the image which is represented here as x1, x2 and so on.", 'start': 813.74, 'duration': 10.246}, {'end': 831.869, 'text': 'each of the inputs are multiplied by what is known as weights, which are represented as w1, w2 and and so on.', 'start': 823.986, 'duration': 7.883}, {'end': 833.65, 'text': 'there is in the central unit.', 'start': 831.869, 'duration': 1.781}, {'end': 842.892, 'text': 'basically there is a summation of these weighted inputs, which is like x1 into w1, plus x2 into w2 and so on.', 'start': 833.65, 'duration': 9.242}], 'summary': 'Artificial neural network mimics human brain using artificial neurons with weighted inputs.', 'duration': 51.884, 'max_score': 791.008, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI791008.jpg'}, {'end': 896.324, 'src': 'embed', 'start': 868.525, 'weight': 7, 'content': [{'end': 872.888, 'text': "but let's do a quick comparison between biological and artificial neuron.", 'start': 868.525, 'duration': 4.363}, {'end': 879.692, 'text': 'just like a biological neuron, there are dendrites and then there is a cell nucleus and synapse and an axon.', 'start': 872.888, 'duration': 6.804}, {'end': 882.374, 'text': 'we have in the artificial neuron as well.', 'start': 879.692, 'duration': 2.682}, {'end': 887.918, 'text': 'these inputs come like the dendrite, if you will act like the dendrites.', 'start': 882.974, 'duration': 4.944}, {'end': 896.324, 'text': 'there is a, like a central unit which performs the summation of these weighted inputs, which is basically w1, x1, w2, x2 and so on,', 'start': 887.918, 'duration': 8.406}], 'summary': 'Comparison of biological and artificial neurons, with inputs and weighted summation.', 'duration': 27.799, 'max_score': 868.525, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI868525.jpg'}, {'end': 964.505, 'src': 'embed', 'start': 932.592, 'weight': 5, 'content': [{'end': 933.733, 'text': "So, that's the output.", 'start': 932.592, 'duration': 1.141}, {'end': 941.416, 'text': 'Now, the whole exercise of training a neuron is about changing these weights and biases.', 'start': 933.793, 'duration': 7.623}, {'end': 945.759, 'text': 'As I mentioned, artificial neural network will consist of several such neurons.', 'start': 941.717, 'duration': 4.042}, {'end': 949.961, 'text': 'And as a part of the training process, these weights keep changing.', 'start': 946.019, 'duration': 3.942}, {'end': 952.462, 'text': 'Initially, they are assigned some random values.', 'start': 950.301, 'duration': 2.161}, {'end': 954.923, 'text': 'through the training process, the weights.', 'start': 952.762, 'duration': 2.161}, {'end': 964.505, 'text': 'the whole process of training is to come up with the optimum values of W1, W2 and Wn, and then the B for, or the bias for this particular neuron,', 'start': 954.923, 'duration': 9.582}], 'summary': 'Training a neuron involves adjusting weights and biases to optimize values for w1, w2, wn, and b.', 'duration': 31.913, 'max_score': 932.592, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI932592.jpg'}], 'start': 514.78, 'title': 'Deep learning applications and neural network basics', 'summary': 'Covers diverse applications of deep learning like real-time language translation, music composition, and image colorization, and also explains the structure of biological neurons, training of artificial neurons, and the role of weights and biases in artificial neural networks.', 'chapters': [{'end': 704.382, 'start': 514.78, 'title': 'Applications of deep learning', 'summary': 'Discusses the diverse applications of deep learning, including real-time language translation, music composition, and image colorization, highlighting the potential for on-demand translation and the ability to train neural nets for music generation and image colorization.', 'duration': 189.602, 'highlights': ['Deep learning is utilized in real-time language translation, enabling on-demand translation of documents and signboards, with potential applications in mobile and web platforms.', 'Neural nets are being trained for music composition and generation, showcasing the potential for innovative music creation through deep learning.', 'Colorization of images using deep learning demonstrates the capability to train neural nets for adding color to grayscale images, showcasing the diverse applications of deep learning in image processing.', 'The chapter emphasizes the potential for on-demand translation using deep learning, highlighting the challenge of manually translating documents into multiple languages and the efficiency of real-time translation systems.', "Deep learning's application in music composition and generation is highlighted, showcasing the potential for training neural nets to produce music, offering a novel approach to music creation.", "The chapter discusses the potential of neural networks as the 'secret sauce' of deep learning, emphasizing their pivotal role in enabling the diverse applications of deep learning."]}, {'end': 1117.097, 'start': 704.382, 'title': 'Neural network basics', 'summary': 'Explains the structure of biological neurons and their comparison with artificial neurons, the process of training artificial neurons, and the role of weights and biases in artificial neural networks.', 'duration': 412.715, 'highlights': ['The smallest component of an artificial neural network is an artificial neuron, also referred to as a perceptron, which receives inputs, performs a weighted summation, and passes the result through an activation function to produce an output in a binary format. The artificial neuron, or perceptron, receives inputs, performs a weighted summation, adds a bias, and passes the result through an activation function to produce a binary output, representing whether the neuron should be fired or not.', 'The training process of artificial neurons involves adjusting the weights and biases based on the feedback received from the output, aiming to attain optimum values that provide accurate outputs. During the training process, the weights and biases of artificial neurons are adjusted based on the feedback received from the output, aiming to achieve optimum values that produce accurate outputs.', 'The inputs, represented by pixel values in image processing, are multiplied by respective weights, and the weighted sum is added with a bias to produce an output from the artificial neuron. In image processing, the inputs, represented by pixel values, are multiplied by respective weights, and the weighted sum is added with a bias to produce an output from the artificial neuron.', 'A biological neuron consists of the cell nucleus, dendrites, axon, and synapses, while an artificial neuron comprises inputs, weighted summation, bias addition, and an activation function to produce an output. The structure of a biological neuron, including the cell nucleus, dendrites, axon, and synapses, is compared with the components of an artificial neuron, which includes inputs, weighted summation, bias addition, and an activation function to produce an output.', 'The training process of artificial neurons involves adjusting the weights and biases based on the feedback received from the output, aiming to attain optimum values that provide accurate outputs. The training process of artificial neurons involves adjusting the weights and biases based on the feedback received from the output, aiming to attain optimum values that provide accurate outputs.']}], 'duration': 602.317, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI514780.jpg', 'highlights': ['Deep learning enables real-time language translation for documents and signboards, with potential applications in mobile and web platforms.', 'Neural nets are being trained for music composition, showcasing potential for innovative music creation through deep learning.', 'Colorization of images using deep learning demonstrates the capability to train neural nets for adding color to grayscale images.', 'The chapter emphasizes the potential for on-demand translation using deep learning, highlighting the challenge of manually translating documents into multiple languages.', 'The smallest component of an artificial neural network is an artificial neuron, also referred to as a perceptron, which receives inputs, performs a weighted summation, and passes the result through an activation function to produce an output in a binary format.', 'The training process of artificial neurons involves adjusting the weights and biases based on the feedback received from the output, aiming to attain optimum values that provide accurate outputs.', 'The inputs, represented by pixel values in image processing, are multiplied by respective weights, and the weighted sum is added with a bias to produce an output from the artificial neuron.', 'A biological neuron consists of the cell nucleus, dendrites, axon, and synapses, while an artificial neuron comprises inputs, weighted summation, bias addition, and an activation function to produce an output.']}, {'end': 1537.653, 'segs': [{'end': 1220.903, 'src': 'embed', 'start': 1155.638, 'weight': 0, 'content': [{'end': 1162.045, 'text': 'Moreover, why is an activation function required? It is basically required to bring in nonlinearity.', 'start': 1155.638, 'duration': 6.407}, {'end': 1165.348, 'text': "That's the main reason why an activation function is required.", 'start': 1162.165, 'duration': 3.183}, {'end': 1172.236, 'text': 'So, what are the different types of activation functions? There are several types of activation functions, but these are the most common ones.', 'start': 1165.488, 'duration': 6.748}, {'end': 1174.638, 'text': 'These are the ones that are currently in use.', 'start': 1172.436, 'duration': 2.202}, {'end': 1178.162, 'text': 'Sigmoid function was one of the early activation functions.', 'start': 1174.838, 'duration': 3.324}, {'end': 1182.164, 'text': 'But today, ReLU has kind of taken over.', 'start': 1178.642, 'duration': 3.522}, {'end': 1187.286, 'text': 'So ReLU is by far the most popular activation function that is used today.', 'start': 1182.324, 'duration': 4.962}, {'end': 1192.088, 'text': 'But still, sigmoid function is still used in many situations.', 'start': 1187.526, 'duration': 4.562}, {'end': 1198.451, 'text': 'These different types of activation functions are used in different situations based on the kind of problem we are trying to solve.', 'start': 1192.268, 'duration': 6.183}, {'end': 1204.474, 'text': 'So what exactly is the difference between these two? Sigmoid gives the values of the output will be between 0 and 1.', 'start': 1198.571, 'duration': 5.903}, {'end': 1209.457, 'text': 'Threshold function is the value will be 0 up to a certain value.', 'start': 1204.474, 'duration': 4.983}, {'end': 1212.258, 'text': 'And beyond that, this is also known as a step function.', 'start': 1209.677, 'duration': 2.581}, {'end': 1214.599, 'text': 'And beyond that, it will be 1.', 'start': 1212.538, 'duration': 2.061}, {'end': 1217.241, 'text': 'In case of sigmoid, there is a gradual increase.', 'start': 1214.599, 'duration': 2.642}, {'end': 1220.903, 'text': "But in case of threshold, it's like also known as a step function.", 'start': 1217.561, 'duration': 3.342}], 'summary': 'Activation functions like relu and sigmoid bring nonlinearity to neural networks.', 'duration': 65.265, 'max_score': 1155.638, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI1155638.jpg'}, {'end': 1397.614, 'src': 'embed', 'start': 1368.38, 'weight': 3, 'content': [{'end': 1372.482, 'text': 'Normally it is zero but you can also set a different value for the threshold.', 'start': 1368.38, 'duration': 4.102}, {'end': 1378.484, 'text': 'Now, the difference between this and the sigmoid is that here the change is rapid or instantaneous.', 'start': 1372.602, 'duration': 5.882}, {'end': 1387.949, 'text': 'as the x value comes from negative up to zero, it remains zero and at 0, it pretty much immediately increases to 1..', 'start': 1378.484, 'duration': 9.465}, {'end': 1397.614, 'text': 'So, this is a mathematical representation of threshold function, phi x is equal to 1 if x is greater than equal to 0, and 0 if x is less than 0.', 'start': 1387.949, 'duration': 9.665}], 'summary': 'Threshold function sets rapid change at x=0.', 'duration': 29.234, 'max_score': 1368.38, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI1368380.jpg'}, {'end': 1537.653, 'src': 'embed', 'start': 1512.177, 'weight': 2, 'content': [{'end': 1517.14, 'text': 'whereas in case of hyperbolic tangent it goes from minus 1 to 1..', 'start': 1512.177, 'duration': 4.963}, {'end': 1521.303, 'text': 'So, that is the difference between hyperbolic tangent and sigmoid function.', 'start': 1517.14, 'duration': 4.163}, {'end': 1523.785, 'text': 'Otherwise, the shape looks very similar.', 'start': 1521.483, 'duration': 2.302}, {'end': 1531.009, 'text': 'There is a gradual increase unlike the step function where there was an instant increase or instant change.', 'start': 1523.985, 'duration': 7.024}, {'end': 1537.653, 'text': 'Here again, very similar to sigmoid function, the value changes gradually from minus 1 to 1.', 'start': 1531.229, 'duration': 6.424}], 'summary': 'Hyperbolic tangent ranges from -1 to 1, similar to sigmoid function but with gradual change.', 'duration': 25.476, 'max_score': 1512.177, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI1512177.jpg'}], 'start': 1117.097, 'title': 'Activation functions in neural networks', 'summary': 'Explains the role of activation functions in neural networks, including the process of taking a weighted sum plus bias as input to generate a binary output, and discusses different types of activation functions currently in use. it also explores the differences between popular activation functions - relu, sigmoid, threshold, and hyperbolic tangent, highlighting their ranges, shapes, and advantages, with relu being the most popular due to its efficiency and accuracy.', 'chapters': [{'end': 1174.638, 'start': 1117.097, 'title': 'Activation functions in neural networks', 'summary': 'Explains the role of activation functions in neural networks, including the process of taking a weighted sum plus bias as input to generate a binary output, and the necessity of activation functions to introduce nonlinearity. it also discusses different types of activation functions currently in use.', 'duration': 57.541, 'highlights': ['An activation function takes the weighted sum plus bias as input to generate a certain output, and there are different types of activation functions with different outputs.', 'The main reason for needing an activation function is to introduce nonlinearity in the neural network.', 'The chapter discusses the necessity of activation functions and the different types currently in use.']}, {'end': 1537.653, 'start': 1174.838, 'title': 'Comparison of activation functions', 'summary': 'Explores the differences between the popular activation functions - relu, sigmoid, threshold, and hyperbolic tangent, highlighting their ranges, shapes, and advantages, with relu being the most popular due to its efficiency and accuracy.', 'duration': 362.815, 'highlights': ['ReLU function, being the most popular today, has no upper limit, providing greater efficiency and accuracy compared to other activation functions like sigmoid and hyperbolic tangent.', 'Sigmoid function outputs values between 0 and 1, with a gradual increase, and its output value always remains between zero and one.', 'Hyperbolic tangent function is similar to sigmoid in shape but outputs values between -1 and 1, with a gradual change from -1 to 1.', 'Threshold function, also known as a step function, has an instantaneous change from 0 to 1, unlike sigmoid, and can have a threshold value other than zero.']}], 'duration': 420.556, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI1117097.jpg', 'highlights': ['ReLU function is the most popular due to its efficiency and accuracy.', 'Sigmoid function outputs values between 0 and 1, with a gradual increase.', 'Hyperbolic tangent function outputs values between -1 and 1, with a gradual change.', 'Threshold function has an instantaneous change from 0 to 1 and can have a threshold value other than zero.', 'Activation functions introduce nonlinearity in the neural network.']}, {'end': 1848.654, 'segs': [{'end': 1706.969, 'src': 'embed', 'start': 1673.67, 'weight': 1, 'content': [{'end': 1675.432, 'text': 'Every iteration is known as an epoch.', 'start': 1673.67, 'duration': 1.762}, {'end': 1682.435, 'text': 'And each time the weights are dated to make sure that the maximum number of images are classified correctly.', 'start': 1675.692, 'duration': 6.743}, {'end': 1684.416, 'text': 'So, once again, what is the input?', 'start': 1682.615, 'duration': 1.801}, {'end': 1693.42, 'text': 'This input could be like thousand images of cats and dogs, and they are labeled because we know which is a cat and which is a dog.', 'start': 1684.576, 'duration': 8.844}, {'end': 1695.541, 'text': 'And we feed those thousand images.', 'start': 1693.74, 'duration': 1.801}, {'end': 1701.665, 'text': 'The neural network will initially assign some weights and biases for each neuron and it will try to process,', 'start': 1695.801, 'duration': 5.864}, {'end': 1706.969, 'text': 'extract the features from the images and it will try to come up with a prediction for each image.', 'start': 1701.665, 'duration': 5.304}], 'summary': 'Neural network iterates to classify images, starting with initial weights and biases.', 'duration': 33.299, 'max_score': 1673.67, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI1673670.jpg'}, {'end': 1782.954, 'src': 'embed', 'start': 1756.681, 'weight': 0, 'content': [{'end': 1766.583, 'text': 'And slowly and steadily, the accuracy of this network will keep increasing and it may reach probably, you never know, 90 percent, 95 percent.', 'start': 1756.681, 'duration': 9.902}, {'end': 1771.925, 'text': 'And there are several parameters that are known as hyper parameters that need to be changed and tweaked.', 'start': 1766.824, 'duration': 5.101}, {'end': 1774.867, 'text': 'and that is the overall training process.', 'start': 1772.545, 'duration': 2.322}, {'end': 1782.954, 'text': 'and ultimately, at some point we say, okay, you will probably never reach 100 percent accuracy, but then we set a limit saying that, okay,', 'start': 1774.867, 'duration': 8.087}], 'summary': "The network's accuracy may reach 90-95% by tweaking hyper parameters during training.", 'duration': 26.273, 'max_score': 1756.681, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI1756681.jpg'}, {'end': 1844.511, 'src': 'embed', 'start': 1814.938, 'weight': 2, 'content': [{'end': 1817.019, 'text': 'And this is again nothing new in deep learning.', 'start': 1814.938, 'duration': 2.081}, {'end': 1819.02, 'text': 'This was there in machine learning as well.', 'start': 1817.059, 'duration': 1.961}, {'end': 1825.403, 'text': 'So you feed the test images and then find out whether we are getting similar accuracy or not.', 'start': 1819.22, 'duration': 6.183}, {'end': 1827.904, 'text': 'So maybe that accuracy may reduce a little bit.', 'start': 1825.463, 'duration': 2.441}, {'end': 1832.426, 'text': 'While training you may get 98% and then for test you may get 95%.', 'start': 1827.964, 'duration': 4.462}, {'end': 1834.226, 'text': "But there shouldn't be a drastic drop.", 'start': 1832.426, 'duration': 1.8}, {'end': 1840.129, 'text': 'Like for example you get 98% in training and then you get 50% or 40% with the test.', 'start': 1834.347, 'duration': 5.782}, {'end': 1842.55, 'text': 'That means your network has not learnt.', 'start': 1840.289, 'duration': 2.261}, {'end': 1844.511, 'text': 'You may have to retrain your network.', 'start': 1842.79, 'duration': 1.721}], 'summary': 'In deep learning, consistent test accuracy is crucial for successful network learning and retraining may be necessary if accuracy drops drastically.', 'duration': 29.573, 'max_score': 1814.938, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI1814938.jpg'}], 'start': 1537.653, 'title': 'Neural network training process', 'summary': 'Discusses the process of training a four-layer neural network, explaining the iterative training process, the role of labeled training data, the calculation of accuracy through iterations, the usage of hyperparameters, and the testing phase with fresh set of images.', 'chapters': [{'end': 1848.654, 'start': 1537.653, 'title': 'Neural network training process', 'summary': 'Discusses the process of training a neural network, using a four-layer neural network as an example, explaining the iterative training process, the role of labeled training data, the calculation of accuracy through iterations, the usage of hyperparameters, and the testing phase with fresh set of images.', 'duration': 311.001, 'highlights': ['The process of training a neural network involves an iterative process where labeled training data, such as thousand images of cats and dogs, is fed to the network, and the network adjusts its weights and biases through multiple iterations or epochs to maximize the correct classification of images, aiming to improve accuracy from 50% to potentially 95% or higher.', 'During the training process, the neural network adjusts its weights and biases for each neuron in an iterative manner to maximize the correct classification of images, with each iteration, known as an epoch, aimed at increasing the accuracy from the initial 50% to potentially reaching 95% or higher.', 'After the training process, the system undergoes a testing phase where a fresh set of images, not seen during training, is fed to the network to evaluate the accuracy, aiming for a minimal drop in accuracy compared to the training phase, ensuring the network has effectively learned from the training data.', 'The training process involves the usage of hyperparameters that need to be adjusted and tweaked to optimize the accuracy of the neural network, with the ultimate goal of achieving a predetermined accuracy, such as 95%, before concluding the training process.']}], 'duration': 311.001, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI1537653.jpg', 'highlights': ['The process of training a neural network involves an iterative process with labeled training data, aiming to improve accuracy from 50% to potentially 95% or higher.', 'During training, the network adjusts its weights and biases through multiple iterations or epochs to maximize correct image classification.', 'The testing phase involves evaluating accuracy with a fresh set of images, aiming for minimal drop compared to the training phase.', 'Usage of hyperparameters is essential to optimize the accuracy of the neural network, aiming for a predetermined accuracy, such as 95%.']}, {'end': 2282.763, 'segs': [{'end': 1894.919, 'src': 'embed', 'start': 1848.654, 'weight': 1, 'content': [{'end': 1860.042, 'text': 'the whole process is about changing these weights and biases and coming up with the optimal values of these weights and biases so that the accuracy is the maximum possible.', 'start': 1848.654, 'duration': 11.388}, {'end': 1860.542, 'text': 'All right.', 'start': 1860.262, 'duration': 0.28}, {'end': 1864.225, 'text': 'So a little bit more detail about how this whole thing works.', 'start': 1860.642, 'duration': 3.583}, {'end': 1870.969, 'text': 'So this is known as forward propagation, which is the data or the information is going in the forward direction.', 'start': 1864.445, 'duration': 6.524}, {'end': 1879.674, 'text': 'The inputs are taken, weighted summation is done, bias is added here, and then that is fed to the activation function.', 'start': 1871.209, 'duration': 8.465}, {'end': 1882.516, 'text': 'And then that is that comes out as an output.', 'start': 1879.954, 'duration': 2.562}, {'end': 1884.196, 'text': 'So, that is forward propagation.', 'start': 1882.836, 'duration': 1.36}, {'end': 1888.538, 'text': 'And the output is compared with the actual value and that will give us the error.', 'start': 1884.436, 'duration': 4.102}, {'end': 1890.578, 'text': 'The difference between them is the error.', 'start': 1888.738, 'duration': 1.84}, {'end': 1894.919, 'text': 'And in technical terms, that is also known as our cost function.', 'start': 1890.898, 'duration': 4.021}], 'summary': 'Optimizing weights and biases for maximum accuracy in forward propagation.', 'duration': 46.265, 'max_score': 1848.654, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI1848654.jpg'}, {'end': 2007.911, 'src': 'heatmap', 'start': 1934.389, 'weight': 3, 'content': [{'end': 1944.396, 'text': 'So we use what is known as an optimization function to minimize this error and the error itself sent back to the system as feedback,', 'start': 1934.389, 'duration': 10.007}, {'end': 1946.578, 'text': 'and that is known as back propagation.', 'start': 1944.396, 'duration': 2.182}, {'end': 1948.16, 'text': 'And so this is the cost function.', 'start': 1946.818, 'duration': 1.342}, {'end': 1954.146, 'text': 'And how do we optimize the cost function? We use what is known as gradient descent.', 'start': 1948.42, 'duration': 5.726}, {'end': 1964.417, 'text': 'So the gradient descent mechanism identifies how to change the weights and biases so that the cost function is minimized.', 'start': 1954.426, 'duration': 9.991}, {'end': 1968.702, 'text': 'And there is also what is known as the rate of the learning rate.', 'start': 1964.818, 'duration': 3.884}, {'end': 1972.084, 'text': 'That is what is shown here as slower and faster.', 'start': 1968.962, 'duration': 3.122}, {'end': 1975.847, 'text': 'So you need to specify what should be the learning rate.', 'start': 1972.224, 'duration': 3.623}, {'end': 1982.971, 'text': 'Now if the learning rate is very small, then it will probably take very long to train.', 'start': 1976.067, 'duration': 6.904}, {'end': 1991.477, 'text': 'Whereas if the learning rate is very high, then it will appear to be faster, but then it will probably never what is known as converge.', 'start': 1983.132, 'duration': 8.345}, {'end': 1995.42, 'text': 'Now what is convergence? Now we are talking about a few terms here.', 'start': 1991.637, 'duration': 3.783}, {'end': 1997.802, 'text': 'Convergence is like this.', 'start': 1995.76, 'duration': 2.042}, {'end': 2000.144, 'text': 'This is a representation of convergence.', 'start': 1997.922, 'duration': 2.222}, {'end': 2007.911, 'text': 'So the whole idea of gradient descent is to optimize the cost function or minimize the cost function.', 'start': 2000.284, 'duration': 7.627}], 'summary': 'Using optimization and gradient descent to minimize cost function in back propagation for faster convergence.', 'duration': 53.485, 'max_score': 1934.389, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI1934389.jpg'}, {'end': 2198.966, 'src': 'embed', 'start': 2174.745, 'weight': 4, 'content': [{'end': 2182.171, 'text': 'And there can be situations where convergence may not happen in rare cases, but by and large, the network will converge.', 'start': 2174.745, 'duration': 7.426}, {'end': 2188.297, 'text': 'And after maybe a few iterations it could be tens of iterations or hundreds of iterations,', 'start': 2182.211, 'duration': 6.086}, {'end': 2191.359, 'text': 'depending on what exactly the number of iterations can vary.', 'start': 2188.297, 'duration': 3.062}, {'end': 2198.966, 'text': 'And then we say, okay, we are getting a certain accuracy and we say that is our threshold, maybe 90 percent accuracy.', 'start': 2191.8, 'duration': 7.166}], 'summary': 'Network convergence is expected in most cases after tens or hundreds of iterations, aiming for a threshold of 90% accuracy.', 'duration': 24.221, 'max_score': 2174.745, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI2174745.jpg'}, {'end': 2269.103, 'src': 'embed', 'start': 2241.845, 'weight': 0, 'content': [{'end': 2251.333, 'text': 'the training process is nothing but finding the best values of the weights and biases for each and every neuron in the network.', 'start': 2241.845, 'duration': 9.488}, {'end': 2252.494, 'text': "that's all.", 'start': 2251.633, 'duration': 0.861}, {'end': 2262.699, 'text': 'training of neural network consists of finding the optimal values of the weights and biases so that the accuracy is maximum.', 'start': 2252.494, 'duration': 10.205}, {'end': 2266.862, 'text': 'all right, so with that we come to the end of the session.', 'start': 2262.699, 'duration': 4.163}, {'end': 2268.202, 'text': 'we all have a great day.', 'start': 2266.862, 'duration': 1.34}, {'end': 2269.103, 'text': 'thank you very much.', 'start': 2268.202, 'duration': 0.901}], 'summary': 'Neural network training finds optimal weights and biases for maximum accuracy.', 'duration': 27.258, 'max_score': 2241.845, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI2241845.jpg'}], 'start': 1848.654, 'title': 'Neural network training and optimization', 'summary': 'Covers the neural network training process, including forward propagation, weighted summation, activation function, and error calculation, emphasizing the importance of gradient descent, cost function minimization, and learning rate for achieving convergence and a threshold accuracy of 90 percent after tens to hundreds of iterations.', 'chapters': [{'end': 1894.919, 'start': 1848.654, 'title': 'Neural network training process', 'summary': 'Explains the forward propagation process in neural network training which involves weighted summation, bias addition, activation function, and error calculation for optimizing accuracy.', 'duration': 46.265, 'highlights': ['The process involves changing weights and biases to achieve maximum accuracy.', 'Forward propagation includes weighted summation, bias addition, activation function, and error calculation.', 'Output is compared with actual value to calculate the error, also known as the cost function.']}, {'end': 2054.347, 'start': 1895.24, 'title': 'Gradient descent and cost function optimization', 'summary': 'Explains the concept of minimizing the cost function using mean squared error, back propagation, and gradient descent, emphasizing the significance of learning rate in achieving convergence.', 'duration': 159.107, 'highlights': ['Mean squared error is the simplest way of defining the cost function The mean squared error is commonly used to define the cost function for minimizing errors in machine learning.', 'Gradient descent is used to optimize the cost function by adjusting weights and biases The gradient descent mechanism identifies how to change the weights and biases to minimize the cost function, crucial for optimizing machine learning models.', 'Significance of learning rate in achieving convergence The learning rate plays a crucial role in determining the speed and effectiveness of training, with a small learning rate leading to slow convergence and a large learning rate causing oscillation and failure to converge.']}, {'end': 2282.763, 'start': 2054.427, 'title': 'Neural network training process', 'summary': 'Explains the process of training a neural network, including gradient descent mechanism, forward and backward propagation, convergence, and deployment, aiming for a threshold accuracy of 90 percent after tens to hundreds of iterations.', 'duration': 228.336, 'highlights': ['The training process involves finding the best values of the weights and biases for each neuron to maximize accuracy. The training of neural network consists of finding the optimal values of the weights and biases so that the accuracy is maximum.', 'The system aims for a threshold accuracy of 90 percent after tens to hundreds of iterations. After maybe a few iterations it could be tens of iterations or hundreds of iterations, depending on what exactly the number of iterations can vary. And then we say, okay, we are getting a certain accuracy and we say that is our threshold, maybe 90 percent accuracy.', 'The process involves forward propagation from the input layer to the output layer, followed by backward propagation, and the iterative adjustment of weights and biases through gradient descent. The data is going forward from the input layer to the output layer and there is an output and error is calculated, the cost function is calculated and that is fed back as a part of backward propagation. and that whole process repeats once again.']}], 'duration': 434.109, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/FbxTVRfQFuI/pics/FbxTVRfQFuI1848654.jpg', 'highlights': ['The training process involves finding the best values of the weights and biases for each neuron to maximize accuracy.', 'The process involves changing weights and biases to achieve maximum accuracy.', 'Forward propagation includes weighted summation, bias addition, activation function, and error calculation.', 'Gradient descent is used to optimize the cost function by adjusting weights and biases.', 'The system aims for a threshold accuracy of 90 percent after tens to hundreds of iterations.', 'Significance of learning rate in achieving convergence.']}], 'highlights': ['Deep learning utilizes labeled training data to enable accurate recognition of new images.', 'Artificial neural networks are used in deep learning for image recognition, achieving accuracy through training.', 'The process involves extracting features from labeled data to enable consistent recognition across different images.', 'Deep learning uses neural networks and is suitable for handling large amounts of unstructured data, enabling automatic feature engineering.', 'Deep learning is required for image recognition, processing, and analyzing images and voice compared to traditional machine learning.', 'Deep learning can perform complex algorithms and achieve best performance with large amounts of data, avoiding the manual process of feature extraction.', 'Deep learning is applied in healthcare for early cancer detection, expediting the screening process and automating the initial screening, with predictions of extensive use in the next 10 to 15 years.', 'Deep learning is utilized in robotics for object detection, obstacle recognition, and sound interpretation, making it a major area of application.', 'The application of deep learning in self-driving cars involves processing video feed to detect obstacles, recognize traffic signals, and ensure safe driving, with predictions of extensive use in the next 10 to 15 years.', 'Deep learning enables real-time language translation for documents and signboards, with potential applications in mobile and web platforms.', 'Neural nets are being trained for music composition, showcasing potential for innovative music creation through deep learning.', 'Colorization of images using deep learning demonstrates the capability to train neural nets for adding color to grayscale images.', 'The process of training a neural network involves an iterative process with labeled training data, aiming to improve accuracy from 50% to potentially 95% or higher.', 'During training, the network adjusts its weights and biases through multiple iterations or epochs to maximize correct image classification.', 'The testing phase involves evaluating accuracy with a fresh set of images, aiming for minimal drop compared to the training phase.', 'Usage of hyperparameters is essential to optimize the accuracy of the neural network, aiming for a predetermined accuracy, such as 95%.', 'The training process involves finding the best values of the weights and biases for each neuron to maximize accuracy.', 'The process involves changing weights and biases to achieve maximum accuracy.', 'Forward propagation includes weighted summation, bias addition, activation function, and error calculation.', 'Gradient descent is used to optimize the cost function by adjusting weights and biases.', 'The system aims for a threshold accuracy of 90 percent after tens to hundreds of iterations.', 'Significance of learning rate in achieving convergence.']}