title
Neural Network Full Course | Neural Network Tutorial For Beginners | Neural Networks | Simplilearn

description
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=AI-ob1yS9g-Zcs&utm_medium=DescriptionFirstFold&utm_source=youtube 🔥Professional Certificate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=AI-ob1yS9g-Zcs&utm_medium=DescriptionFirstFold&utm_source=youtube This full course video on Neural Network tutorial will help you understand what a neural network is, how it works, and what are the different types of neural networks. You will learn how each neuron processes data, what are activation functions, and how a neuron fires. You will get an idea about backpropagation and gradient descent algorithms. You will have a look at the convolution neural network and how it identifies objects in an image. Finally, you will understand about the recurrent neural networks and lstm in detail. Now, let's get started with learning neural networks. 🔥Free AI Course With Course Completion Certificate: https://www.simplilearn.com/learn-ai-basics-skillup?utm_campaign=AI-ob1yS9g-Zcs&utm_medium=DescriptionFirstFold&utm_source=youtube Dataset Link - https://drive.google.com/drive/folders/11T76B8UkTg9lU-sPhPlWqn6MOVhQ-FjS Below topics are explained in this Neural Network Full Course: 1. Animated Video 00:52 2. What is A Neural Network 06:35 3. What is Deep Learning 07:40 4. What is Artificial Neural Network 09:00 5. How Does Neural Network Works 10:37 6. Advantages of Neural Network 13:39 7. Applications of Neural Network 14:59 8. Future of Neural Network 17:03 9. How Does Neural Network Works 19:10 10. Types of Artificial Neural Network 29:27 11. Use Case-Problem Statement 34:57 12. Use Case-Implementation 36:17 13. Backpropagation & Gradient Descent 01:06:00 14. Loss Fubction 01:10:26 15. Gradient Descent 01:11:26 16. Backpropagation 01:13:07 17. Convolutional Neural Network 01:17:54 18. How Image recognition Works 01:17:58 19. Introduction to CNN 01:20:25 20. What is Convolutional Neural Network 01:20:51 21. How CNN recognize Images 01:25:34 22. Layers in Convolutional Neural Network 01:26:19 23. Use Case implementation using CNN 01:39:21 24. What is a Neural Network 02:21:24 25. Popular Neural Network 02:23:08 26. Why Recurrent Neural Network 02:24:19 27. Applications of Recurrent Neural Network 02:25:32 28. how does a RNN works 02:28:42 29. vanishing And Exploding Gradient Problem 02:31:02 30. Long short term Memory 02:35:54 31. use case implementation of LSTM 02:44:32 To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip #NeuralNetwork #NeuralNetworkFullCourse #NeuralNetworkTutorial #WhatIsNeuralNetwork #DeepLearning #DeepLearningTutorial #DeepLearningCourse #DeepLearningExplained #Simplilearn Simplilearn’s Deep Learning course will transform you into an expert in Deep Learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our Deep Learning course, you'll master Deep Learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as Deep Learning scientist. You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to: 1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline 2. Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Neural-Network-Full-Course-ob1yS9g-Zcs&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn/ - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

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{'title': 'Neural Network Full Course | Neural Network Tutorial For Beginners | Neural Networks | Simplilearn', 'heatmap': [], 'summary': 'Covers neural networks and their applications in real-time translation, image and music composition, and facial recognition, emphasizing future potential in medicine, agriculture, and physics, including image recognition with diverse applications in robotics, image processing, and text-to-speech. it also discusses implementing neural networks for image classification, adjusting parameters for reduced processing time, training for image recognition using convolutional neural networks, data visualization for machine learning, tensorflow model training, rnn and lstm applications, and building and training rnn models for stock price prediction.', 'chapters': [{'end': 1223.427, 'segs': [{'end': 94.37, 'src': 'embed', 'start': 69.21, 'weight': 1, 'content': [{'end': 76.917, 'text': 'Neural networks form the base of deep learning, a subfield of machine learning where the algorithms are inspired by the structure of the human brain.', 'start': 69.21, 'duration': 7.707}, {'end': 86.124, 'text': 'Neural networks take in data, train themselves to recognize the patterns in this data, and then predict the outputs for a new set of similar data.', 'start': 77.497, 'duration': 8.627}, {'end': 88.386, 'text': "Let's understand how this is done.", 'start': 86.765, 'duration': 1.621}, {'end': 94.37, 'text': "Let's construct a neural network that differentiates between a square, circle, and triangle.", 'start': 89.227, 'duration': 5.143}], 'summary': 'Neural networks in deep learning recognize patterns and predict outputs for new data, as demonstrated by differentiating between shapes.', 'duration': 25.16, 'max_score': 69.21, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs69210.jpg'}, {'end': 1062.906, 'src': 'embed', 'start': 1034.554, 'weight': 0, 'content': [{'end': 1036.395, 'text': "I'll tell you what I see in the future.", 'start': 1034.554, 'duration': 1.841}, {'end': 1041.057, 'text': 'more personalized choices for users and customers all over the world.', 'start': 1037.015, 'duration': 4.042}, {'end': 1041.877, 'text': 'i certainly like that.', 'start': 1041.057, 'duration': 0.82}, {'end': 1049.86, 'text': 'when i go in there and whatever online ordering system starts referring stuff to me local company here where i live that uses this,', 'start': 1041.877, 'duration': 7.983}, {'end': 1054.282, 'text': 'where you can take a picture and it starts looking for what you want based on your picture.', 'start': 1049.86, 'duration': 4.422}, {'end': 1057.924, 'text': 'so if you see a couch you like starts looking for furniture like that or clothing.', 'start': 1054.282, 'duration': 3.642}, {'end': 1059.064, 'text': "i think it's mainly clothing.", 'start': 1057.924, 'duration': 1.14}, {'end': 1062.906, 'text': 'hyper intelligent virtual assistants will make life easier.', 'start': 1059.064, 'duration': 3.842}], 'summary': 'In the future, there will be more personalized choices for users and customers globally, including an online ordering system with local company referrals and hyper-intelligent virtual assistants.', 'duration': 28.352, 'max_score': 1034.554, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs1034554.jpg'}, {'end': 1197.17, 'src': 'embed', 'start': 1168.537, 'weight': 6, 'content': [{'end': 1172.08, 'text': 'The green layer is the input, so you have your data coming in.', 'start': 1168.537, 'duration': 3.543}, {'end': 1175.563, 'text': 'It picks up the input signals and passes them to the next layer.', 'start': 1172.2, 'duration': 3.363}, {'end': 1179.646, 'text': 'The next layer does all kinds of calculations and feature extraction.', 'start': 1175.703, 'duration': 3.943}, {'end': 1180.807, 'text': "It's called the hidden layer.", 'start': 1179.706, 'duration': 1.101}, {'end': 1183.41, 'text': "A lot of times there's more than one hidden layer.", 'start': 1181.067, 'duration': 2.343}, {'end': 1188.618, 'text': "We're only showing one in this picture, but we'll show you how it looks like in more detail in a little bit.", 'start': 1183.591, 'duration': 5.027}, {'end': 1190.821, 'text': 'And then finally we have an output layer.', 'start': 1188.898, 'duration': 1.923}, {'end': 1193.605, 'text': 'This layer delivers the final result.', 'start': 1191.101, 'duration': 2.504}, {'end': 1197.17, 'text': 'So the only two things we see is the input layer and the output layer.', 'start': 1194.046, 'duration': 3.124}], 'summary': 'Neural network: input layer receives data, hidden layer processes, output layer delivers final result.', 'duration': 28.633, 'max_score': 1168.537, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs1168537.jpg'}], 'start': 5.245, 'title': 'Neural networks and their applications', 'summary': 'Provides an overview of neural networks, covering practical implementation, applications in real-time translation, image and music composition, facial recognition, deep learning, artificial neural networks, advantages such as fault tolerance and efficient computational solutions, and future potential applications in medicine, agriculture, and physics.', 'chapters': [{'end': 344.18, 'start': 5.245, 'title': 'Neural networks overview', 'summary': "Explains the basics of neural networks, emphasizing their practical implementation, including back propagation and gradient descent, and delving into the applications of convolutional and recurring neural networks, citing examples such as google's real-time translation, image and music composition, and facial recognition on smartphones.", 'duration': 338.935, 'highlights': ['Neural networks form the base of deep learning, a subfield of machine learning where the algorithms are inspired by the structure of the human brain. This highlights the foundational role of neural networks in deep learning, providing context for their significance.', 'Neural networks learn by example, so we do not need to program it in depth. This emphasizes the learning process of neural networks, indicating their ability to learn from examples rather than requiring extensive programming.', 'Neural networks may take hours or even months to train, but time is a reasonable trade-off when compared to its scope. This highlights the time-intensive nature of training neural networks but underscores the trade-off as reasonable given their potential scope and capabilities.', 'Facial recognition cameras on smartphones can estimate the age of the person based on their facial features, showcasing neural networks at play in differentiating faces from the background and correlating facial features to forecast age. This illustrates the practical application of neural networks in facial recognition, specifically in estimating age based on facial features.', 'Neural networks are trained to understand patterns and detect the possibility of rainfall or a rise in stock prices with high accuracy. This highlights the ability of neural networks to detect patterns and make accurate forecasts, such as predicting rainfall or stock price changes.']}, {'end': 795.291, 'start': 344.761, 'title': 'Neural networks: basics and future', 'summary': 'Discusses the basics of neural networks, including deep learning, artificial neural networks, and their future, highlighting the key developments in the field and the potential of replicating the human brain, while also emphasizing the practical applications and advantages of neural networks.', 'duration': 450.53, 'highlights': ['Companies like Google, Amazon, and Nvidia have invested in developing products to support neural networks. Big names like Google, Amazon, and Nvidia have made significant investments in developing products such as libraries, predictive models, and intuitive GPUs to support neural networks.', 'Deep learning was achieved after 2000, enabling recognition of images and videos. After 2000, deep learning was achieved, allowing for the recognition of different images and videos, marking a significant development in the field.', 'Neural networks are a system patterned after the operation of neurons in the human brain and are a way to achieve deep learning. Neural networks, modeled after the operation of neurons in the human brain, are a means of achieving deep learning, signifying a key advancement in this field.', 'Artificial neural networks utilize input layers, hidden layers, and output layers for information processing. Artificial neural networks leverage input layers, hidden layers, and output layers for processing information, demonstrating a fundamental aspect of their functionality.', 'The chapter provides a comprehensive overview of deep learning, artificial neural networks, and their applications. The chapter offers a detailed overview of deep learning, artificial neural networks, and their practical applications, providing valuable insights into the field.']}, {'end': 1000.298, 'start': 795.591, 'title': 'Advantages of artificial neural network', 'summary': 'Discusses the process of artificial neural networks and highlights their advantages, including high fault tolerance, autonomous debugging, and efficient computational solutions, along with real-life applications such as handwriting recognition, stock exchange prediction, and solving traveling salesman problem.', 'duration': 204.707, 'highlights': ['Artificial neural networks have the potential for high fault tolerance. They are capable of withstanding faults or errors in the system, enhancing reliability and stability.', 'Neural network helps solve the traveling salesman problem, providing higher revenue at a minimal cost. It contributes to finding the optimal path for traveling between cities, optimizing revenue and minimizing expenses in logistics.', 'Neural network is used for handwriting recognition, converting handwritten characters into digital ones for system recognition. It aids in the conversion of handwritten characters into digital format, facilitating recognition in various applications.', 'Neural network can examine various factors and predict stock prices on a daily basis, assisting stockbrokers in understanding the stock market. It assists in analyzing multiple factors to predict stock prices daily, aiding stockbrokers in navigating the complex stock market.', "Artificial neural network outputs aren't limited by inputs and results given initially by an expert system, which is beneficial for robotics and pattern recognition systems. This capability allows for flexibility in outputs, particularly advantageous for robotics and pattern recognition applications."]}, {'end': 1223.427, 'start': 1000.578, 'title': 'Future of neural networks', 'summary': 'Discusses the future applications of neural networks including image compression, personalized choices for users, faster neural networks, and the workings of a neural network, with potential applications in various fields such as medicine, agriculture, and physics.', 'duration': 222.849, 'highlights': ['Neural networks will be used in the field of medicine, agriculture, physics, discoveries, and more, making it accessible for anyone to access and process data. Neural networks are expected to have applications in various fields, making data processing accessible and aiding in diverse areas such as medicine, agriculture, and physics.', 'Future personalized choices for users and customers all over the world, with applications in online ordering systems and hyper-intelligent virtual assistants. The future will offer more personalized choices for users and customers, including applications in online ordering systems and the development of hyper-intelligent virtual assistants.', 'Explanation of how a neural network works, including the input layer, hidden layer for feature extraction, and output layer delivering the final result. The chapter explains the functioning of a neural network, highlighting the input layer, hidden layer for feature extraction, and the output layer delivering final results.', 'Prediction of faster neural networks in the future, with tools embedded in every design surface and potential applications in various fields. The future is expected to bring faster neural networks with embedded tools and applications across a wide range of fields.']}], 'duration': 1218.182, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs5245.jpg', 'highlights': ['Neural networks form the base of deep learning, a subfield of machine learning where the algorithms are inspired by the structure of the human brain.', 'Facial recognition cameras on smartphones can estimate the age of the person based on their facial features, showcasing neural networks at play in differentiating faces from the background and correlating facial features to forecast age.', 'Neural networks are expected to have applications in various fields, making data processing accessible and aiding in diverse areas such as medicine, agriculture, and physics.', 'Companies like Google, Amazon, and Nvidia have invested in developing products to support neural networks.', 'Artificial neural networks utilize input layers, hidden layers, and output layers for information processing.', 'Neural networks are trained to understand patterns and detect the possibility of rainfall or a rise in stock prices with high accuracy.', 'The future will offer more personalized choices for users and customers, including applications in online ordering systems and the development of hyper-intelligent virtual assistants.', 'Neural networks learn by example, so we do not need to program it in depth.', 'Artificial neural networks have the potential for high fault tolerance.', 'Neural networks may take hours or even months to train, but time is a reasonable trade-off when compared to its scope.']}, {'end': 2152.711, 'segs': [{'end': 1976.643, 'src': 'embed', 'start': 1937.463, 'weight': 0, 'content': [{'end': 1943.046, 'text': "I'd like to play a song on my Pandora and I'd like it to be at volume ninety percent,", 'start': 1937.463, 'duration': 5.583}, {'end': 1946.088, 'text': 'so you now can add different things in there and it connects them together.', 'start': 1943.046, 'duration': 3.042}, {'end': 1949.41, 'text': 'the input features are taken in batches like a filter.', 'start': 1946.088, 'duration': 3.322}, {'end': 1953.992, 'text': 'this allows a network to remember an image in parts convolution, neural network,', 'start': 1949.41, 'duration': 4.582}, {'end': 1958.954, 'text': "Today's world in photo identification and taking apart photos and trying to.", 'start': 1954.392, 'duration': 4.562}, {'end': 1961.976, 'text': 'you know, have you ever seen that on Google, where you have five people together?', 'start': 1958.954, 'duration': 3.022}, {'end': 1965.177, 'text': 'This is the kind of thing that separates all those people.', 'start': 1962.136, 'duration': 3.041}, {'end': 1967.118, 'text': 'so then it can do a face recognition on each person.', 'start': 1965.177, 'duration': 1.941}, {'end': 1970.18, 'text': 'Applications used in signal and image processing.', 'start': 1967.238, 'duration': 2.942}, {'end': 1974.662, 'text': 'In this case, I used facial images or Google picture images as one of the options.', 'start': 1970.3, 'duration': 4.362}, {'end': 1976.643, 'text': 'Modular neural network.', 'start': 1975.122, 'duration': 1.521}], 'summary': 'Pandora song played at 90% volume, neural network for photo identification and face recognition, applied in signal and image processing.', 'duration': 39.18, 'max_score': 1937.463, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs1937463.jpg'}, {'end': 2073.545, 'src': 'embed', 'start': 2036.404, 'weight': 2, 'content': [{'end': 2042.148, 'text': "There's not really a science to it because each specific domain has different things it's looking at.", 'start': 2036.404, 'duration': 5.744}, {'end': 2047.752, 'text': "So if you're in the banking domain, it's going to be different than the medical domain, than the automatic car domain.", 'start': 2042.288, 'duration': 5.464}, {'end': 2052.235, 'text': 'And suddenly figuring out how those all fit together is just a lot of fun and really cool.', 'start': 2047.912, 'duration': 4.323}, {'end': 2054.795, 'text': 'So we have our types of artificial neural network.', 'start': 2052.534, 'duration': 2.261}, {'end': 2056.757, 'text': 'We have our feedforward neural network.', 'start': 2054.916, 'duration': 1.841}, {'end': 2059.158, 'text': 'We have a radial basis function neural network.', 'start': 2056.937, 'duration': 2.221}, {'end': 2064.159, 'text': 'We have our cohenin, self-organizing neural network, recurrent neural network,', 'start': 2059.217, 'duration': 4.942}, {'end': 2068.902, 'text': 'convolution neural network and modular neural network where it brings them all together.', 'start': 2064.159, 'duration': 4.743}, {'end': 2073.545, 'text': 'And no, the colors on the brain do not match what your brain actually does,', 'start': 2069.223, 'duration': 4.322}], 'summary': 'Different domains have unique considerations for neural networks; various types of neural networks are used, including feedforward, radial basis function, cohenin, self-organizing, recurrent, convolution, and modular networks.', 'duration': 37.141, 'max_score': 2036.404, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs2036404.jpg'}, {'end': 2147.327, 'src': 'embed', 'start': 2113.639, 'weight': 1, 'content': [{'end': 2114.7, 'text': 'That is my computer.', 'start': 2113.639, 'duration': 1.061}, {'end': 2117.402, 'text': 'I have sticky notes on my computer in different colors.', 'start': 2114.78, 'duration': 2.622}, {'end': 2120.004, 'text': "So not too far from today's programmer.", 'start': 2117.602, 'duration': 2.402}, {'end': 2125.289, 'text': 'So the problem is we want to classify photos of cats and dogs using a neural network.', 'start': 2120.144, 'duration': 5.145}, {'end': 2134.636, 'text': 'and you can see over here, we have quite a variety of dogs in the pictures and cats, and you know, just sorting out it is a cat is pretty amazing.', 'start': 2125.769, 'duration': 8.867}, {'end': 2137.779, 'text': 'and why would anybody want to even know the difference between a cat and a dog?', 'start': 2134.636, 'duration': 3.143}, {'end': 2138.88, 'text': 'okay, you know why.', 'start': 2137.779, 'duration': 1.101}, {'end': 2140.161, 'text': 'well, I have a cat door.', 'start': 2138.88, 'duration': 1.281}, {'end': 2147.327, 'text': "it'd be kind of fun that instead of it identifying, instead of having like a little collar with a magnet on it, which is what my cat has,", 'start': 2140.161, 'duration': 7.166}], 'summary': 'Training a neural network to classify cats and dogs in photos.', 'duration': 33.688, 'max_score': 2113.639, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs2113639.jpg'}], 'start': 1223.507, 'title': 'Neural networks and applications', 'summary': 'Covers image recognition with neural networks, basics of neural networks including layers and functions, and various types of neural networks with applications in robotics, image processing, and text-to-speech, highlighting their diverse applications and cutting-edge connections.', 'chapters': [{'end': 1296.106, 'start': 1223.507, 'title': 'Image recognition and neural networks', 'summary': 'Discusses the process of feeding a 28x28 pixel image as input to identify a registration plate using neural networks, with pixel activation values ranging from 0 to 1 and the potential for direct image input into certain neural networks.', 'duration': 72.599, 'highlights': ['The image is fed as a 28x28 pixel input to identify the registration plate using neural networks. 28x28 pixel input, identify registration plate, neural networks', 'Pixel activation values range from 0 to 1, with 1 representing white and 0 representing black pixels. pixel activation range, grayscale values, 0 to 1', 'The potential for direct image input into certain neural networks is noted, eliminating the need for converting image rows into one array. direct image input, neural networks, image preprocessing']}, {'end': 1902.871, 'start': 1296.306, 'title': 'Neural network basics', 'summary': 'Explains the basics of neural networks, covering concepts such as input layer, hidden layers, activation functions, and backpropagation, while also discussing various types of artificial neural networks and their applications.', 'duration': 606.565, 'highlights': ['The input layer passes it to the hidden layer, with two hidden layers having interconnections assigned weights at random. The input layer passes data to the hidden layer, where two hidden layers with interconnections are assigned weights at random.', 'The weighted sum of the input is fed as an input to the activation function to decide which nodes to fire and for feature extraction within the hidden layers. The weighted sum of the input is used as input to the activation function to determine which nodes to fire and for feature extraction within the hidden layers.', 'The model predicts the outcome by applying a suitable activation function to the output layer, and error in the output is back-propagated through the network for weight adjustments to minimize the error rate. The model predicts the outcome by applying an activation function to the output layer, and any error in the output is back-propagated through the network to adjust weights and minimize the error rate.', "Multiple iterations are done to compare the output with the original result, adjusting the weights at every interconnection based on the error, while exploring different types of artificial neural networks such as feed-forward, radial basis function, and Cajonin's Self-Organizing Neural Network. Multiple iterations are performed to compare the output with the original result and adjust weights based on the error. Additionally, the chapter explores various types of artificial neural networks including feed-forward, radial basis function, and Cajonin's Self-Organizing Neural Network."]}, {'end': 2152.711, 'start': 1903.271, 'title': 'Neural networks: types, applications, and use cases', 'summary': 'Discusses various types of neural networks, their applications in robotics, image processing, and text-to-speech, and the use of neural networks to classify photos of cats and dogs for practical purposes, highlighting their varied applications and the cutting-edge nature of neural network connections.', 'duration': 249.44, 'highlights': ['The chapter discusses various types of neural networks, such as feedforward, recurrent, convolution, and modular neural networks, along with their applications in robotics, image processing, and text-to-speech. The chapter covers different types of neural networks and their applications, including feedforward, recurrent, convolution, and modular neural networks, in domains such as robotics, image processing, and text-to-speech.', 'The use of neural networks to classify photos of cats and dogs is presented as a practical application, with the example of a cat door being able to differentiate between a cat and a dog. The chapter demonstrates the practical use of neural networks in classifying photos of cats and dogs, exemplifying the potential application in distinguishing between different animals, such as for a cat door.', 'The concept of pipeline in neural networks is highlighted, emphasizing the experimental and cutting-edge nature of connecting neural networks and the creative aspect of fitting domains together. The discussion emphasizes the experimental nature of neural network connections, particularly the concept of pipeline, and highlights the creativity involved in fitting different domains together.']}], 'duration': 929.204, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs1223507.jpg', 'highlights': ['The chapter covers different types of neural networks and their applications, including feedforward, recurrent, convolution, and modular neural networks, in domains such as robotics, image processing, and text-to-speech.', 'The model predicts the outcome by applying an activation function to the output layer, and any error in the output is back-propagated through the network to adjust weights and minimize the error rate.', 'The input layer passes data to the hidden layer, where two hidden layers with interconnections are assigned weights at random.', 'The discussion emphasizes the experimental nature of neural network connections, particularly the concept of pipeline, and highlights the creativity involved in fitting different domains together.', 'The image is fed as a 28x28 pixel input to identify the registration plate using neural networks.']}, {'end': 3445.553, 'segs': [{'end': 2181.951, 'src': 'embed', 'start': 2152.711, 'weight': 2, 'content': [{'end': 2154.093, 'text': "that's the dog i want to let in.", 'start': 2152.711, 'duration': 1.382}, {'end': 2157.055, 'text': "maybe i don't want to let this other animal in because it's a raccoon.", 'start': 2154.093, 'duration': 2.962}, {'end': 2160.618, 'text': 'so you can see where you could take this one step further and actually apply this.', 'start': 2157.055, 'duration': 3.563}, {'end': 2164.482, 'text': 'you could actually start a little startup company idea self-identifying door.', 'start': 2160.618, 'duration': 3.864}, {'end': 2168.504, 'text': 'So this use case will be implemented on Python.', 'start': 2164.822, 'duration': 3.682}, {'end': 2170.985, 'text': 'I am actually in Python 3.6.', 'start': 2168.664, 'duration': 2.321}, {'end': 2177.049, 'text': "It's always nice to tell people the version of Python because it does affect sometimes which modules you load and everything.", 'start': 2170.985, 'duration': 6.064}, {'end': 2179.89, 'text': "And we're going to start by importing the required packages.", 'start': 2177.149, 'duration': 2.741}, {'end': 2181.951, 'text': "I told you we're going to do this in Keras.", 'start': 2180.37, 'duration': 1.581}], 'summary': 'Using python 3.6, a self-identifying door is being implemented in keras.', 'duration': 29.24, 'max_score': 2152.711, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs2152711.jpg'}, {'end': 2237.087, 'src': 'embed', 'start': 2214.921, 'weight': 6, 'content': [{'end': 2223.33, 'text': "And the first thing you'll notice is that Keras runs on top of either TensorFlow, CNTK, and I think it's pronounced Thano or Theano.", 'start': 2214.921, 'duration': 8.409}, {'end': 2227.555, 'text': "What's important on here is that TensorFlow and the same is true for all these,", 'start': 2223.55, 'duration': 4.005}, {'end': 2232.561, 'text': 'but TensorFlow is probably one of the most widely used currently packages out there with the Keras.', 'start': 2227.555, 'duration': 5.006}, {'end': 2234.724, 'text': 'And of course, tomorrow this is all going to change.', 'start': 2232.822, 'duration': 1.902}, {'end': 2237.087, 'text': "It's all going to disappear and they'll have something new out there.", 'start': 2234.804, 'duration': 2.283}], 'summary': 'Keras runs on top of tensorflow, cntk, and possibly theano, with tensorflow being widely used with keras.', 'duration': 22.166, 'max_score': 2214.921, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs2214921.jpg'}, {'end': 2373.279, 'src': 'embed', 'start': 2338.033, 'weight': 7, 'content': [{'end': 2341.094, 'text': "And the first thing, as you can see right here, there's a lot of dependencies.", 'start': 2338.033, 'duration': 3.061}, {'end': 2344.214, 'text': "A lot of these you should recognize by now if you've done any of these videos.", 'start': 2341.334, 'duration': 2.88}, {'end': 2346.855, 'text': 'If not, kudos for you for jumping in today.', 'start': 2344.574, 'duration': 2.281}, {'end': 2350.615, 'text': 'pip install numpy scipy the scikit-learn.', 'start': 2347.035, 'duration': 3.58}, {'end': 2357.616, 'text': 'Pillow and h5py are both needed for the TensorFlow and then putting the Keras on there.', 'start': 2350.975, 'duration': 6.641}, {'end': 2362.177, 'text': "And then you'll see here, and pip is just a standard installer that you use with Python.", 'start': 2357.756, 'duration': 4.421}, {'end': 2367.078, 'text': "You'll see here that we did pip install TensorFlow since we're going to do Keras on top of TensorFlow.", 'start': 2362.277, 'duration': 4.801}, {'end': 2370.059, 'text': 'and then pip install, and I went ahead and used the GitHub.', 'start': 2367.338, 'duration': 2.721}, {'end': 2373.279, 'text': "So git plus git, and you'll see here github.com.", 'start': 2370.379, 'duration': 2.9}], 'summary': 'Installing dependencies like numpy, scipy, scikit-learn, tensorflow, keras using pip and github.', 'duration': 35.246, 'max_score': 2338.033, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs2338033.jpg'}, {'end': 2464.866, 'src': 'embed', 'start': 2440.363, 'weight': 5, 'content': [{'end': 2445.909, 'text': "You can use any kind of Python editor, whatever setup you're comfortable with and whatever you're doing in there.", 'start': 2440.363, 'duration': 5.546}, {'end': 2449.113, 'text': "So let's go ahead and go in here and paste the code in.", 'start': 2446.29, 'duration': 2.823}, {'end': 2452.497, 'text': "And we're importing a number of different settings in here.", 'start': 2449.534, 'duration': 2.963}, {'end': 2454.199, 'text': 'We have import sequential.', 'start': 2452.637, 'duration': 1.562}, {'end': 2458.284, 'text': "That's under the models because that's the model we're going to use as far as our neural network.", 'start': 2454.459, 'duration': 3.825}, {'end': 2464.866, 'text': 'And then we have layers and we have conversion 2D, max pooling 2D, flatten, dense.', 'start': 2458.704, 'duration': 6.162}], 'summary': 'Using python editor to import sequential, layers, and other settings for neural network.', 'duration': 24.503, 'max_score': 2440.363, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs2440363.jpg'}, {'end': 3096.873, 'src': 'embed', 'start': 3069.953, 'weight': 10, 'content': [{'end': 3075.616, 'text': "And we've compiled them as far as how it trains to use these settings for the training, backpropagation.", 'start': 3069.953, 'duration': 5.663}, {'end': 3079.579, 'text': 'So if you remember, we talked about training our setup.', 'start': 3075.896, 'duration': 3.683}, {'end': 3082.942, 'text': "And when we go into this, you'll see that we have two data sets.", 'start': 3079.9, 'duration': 3.042}, {'end': 3085.805, 'text': 'We have one called the training set and the testing set.', 'start': 3083.062, 'duration': 2.743}, {'end': 3089.308, 'text': "And that's very standard in any data processing.", 'start': 3086.105, 'duration': 3.203}, {'end': 3090.129, 'text': 'is you need to have?', 'start': 3089.308, 'duration': 0.821}, {'end': 3092.951, 'text': "that's pretty common in any data processing.", 'start': 3090.129, 'duration': 2.822}, {'end': 3096.873, 'text': "is you need to have a certain amount of data to train it, and then you've got to know whether it works or not?", 'start': 3092.951, 'duration': 3.922}], 'summary': 'Training involves two datasets: training and testing, a standard practice in data processing.', 'duration': 26.92, 'max_score': 3069.953, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs3069953.jpg'}, {'end': 3172.73, 'src': 'embed', 'start': 3145.594, 'weight': 1, 'content': [{'end': 3151.919, 'text': "And you'll see here we have one point which tells us it's a float value on the rescale over 255.", 'start': 3145.594, 'duration': 6.325}, {'end': 3156.423, 'text': "Where does 255 come from? Well, that's the scale in the colors of the pictures we're using.", 'start': 3151.919, 'duration': 4.504}, {'end': 3159.665, 'text': 'Their value from 0 to 255.', 'start': 3156.583, 'duration': 3.082}, {'end': 3164.067, 'text': 'So we want to divide it by 255 and it will generate a number between 0 and 1.', 'start': 3159.665, 'duration': 4.402}, {'end': 3172.73, 'text': 'They have shear range and zoom range, horizontal flip equals true, and this of course has to do with if the photos are different shapes and sizes.', 'start': 3164.067, 'duration': 8.663}], 'summary': 'Image processing involves rescaling to generate numbers between 0 and 1.', 'duration': 27.136, 'max_score': 3145.594, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs3145594.jpg'}, {'end': 3213.783, 'src': 'embed', 'start': 3182.153, 'weight': 3, 'content': [{'end': 3183.874, 'text': 'And let me go ahead and run this code.', 'start': 3182.153, 'duration': 1.721}, {'end': 3187.915, 'text': "And again, it doesn't really do anything because we're still setting up the pre-processing.", 'start': 3184.074, 'duration': 3.841}, {'end': 3190.156, 'text': "Let's take a look at this next set of code.", 'start': 3188.015, 'duration': 2.141}, {'end': 3191.818, 'text': 'And this one is just huge.', 'start': 3190.497, 'duration': 1.321}, {'end': 3193.619, 'text': "We're creating the training set.", 'start': 3191.998, 'duration': 1.621}, {'end': 3199.903, 'text': "So the training set is going to go in here and it's going to use our train data gen we just created, .", 'start': 3194.079, 'duration': 5.824}, {'end': 3201.004, 'text': 'flow from directory.', 'start': 3199.903, 'duration': 1.101}, {'end': 3205.867, 'text': "It's going to access, in this case, the path, data set, training set.", 'start': 3201.144, 'duration': 4.723}, {'end': 3206.908, 'text': "That's a folder.", 'start': 3206.187, 'duration': 0.721}, {'end': 3209.782, 'text': "So it's going to pull all the images out of that folder.", 'start': 3207.301, 'duration': 2.481}, {'end': 3213.783, 'text': "Now, I'm actually running this in the folder that the data sets in.", 'start': 3209.962, 'duration': 3.821}], 'summary': 'Setting up pre-processing and creating the training set for images.', 'duration': 31.63, 'max_score': 3182.153, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs3182153.jpg'}, {'end': 3243.113, 'src': 'embed', 'start': 3219.404, 'weight': 0, 'content': [{'end': 3226.425, 'text': 'wherever your Jupyter notebook is saving things to, that you create this path, or you can do the complete path if you need to.', 'start': 3219.404, 'duration': 7.021}, {'end': 3228.426, 'text': 'you know C, colon slash, et cetera.', 'start': 3226.425, 'duration': 2.001}, {'end': 3232.648, 'text': 'And the target size, the batch size, and class mode is binary.', 'start': 3228.706, 'duration': 3.942}, {'end': 3235.469, 'text': "So the classes, we're switching everything to a binary value.", 'start': 3232.908, 'duration': 2.561}, {'end': 3240.612, 'text': "Batch size, what the heck is batch size? Well, that's how many pictures we're going to batch through the training each time.", 'start': 3235.63, 'duration': 4.982}, {'end': 3243.113, 'text': 'And the target size, 64 by 64.', 'start': 3240.812, 'duration': 2.301}], 'summary': 'Configuring jupyter notebook for binary classification with target size 64 by 64 and batch size training.', 'duration': 23.709, 'max_score': 3219.404, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs3219404.jpg'}, {'end': 3433.907, 'src': 'embed', 'start': 3404.636, 'weight': 9, 'content': [{'end': 3406.038, 'text': "And we're going to fit our data.", 'start': 3404.636, 'duration': 1.402}, {'end': 3411.819, 'text': 'And as it goes, it says Epic 1 of 25, you start realizing that this is going to take a while.', 'start': 3406.417, 'duration': 5.402}, {'end': 3415.921, 'text': 'On my older computer, it takes about 45 minutes.', 'start': 3412.139, 'duration': 3.782}, {'end': 3418.081, 'text': 'I have a dual processor.', 'start': 3416.281, 'duration': 1.8}, {'end': 3421.443, 'text': "We're processing 10, 000 photos.", 'start': 3418.362, 'duration': 3.081}, {'end': 3424.064, 'text': "That's not a small amount of photographs to process.", 'start': 3421.683, 'duration': 2.381}, {'end': 3427.625, 'text': "So if you're on your laptop, which I am, it's going to take a while.", 'start': 3424.364, 'duration': 3.261}, {'end': 3432.647, 'text': "So let's go ahead and go get our cup of coffee and a sip and come back and see what this looks like.", 'start': 3427.805, 'duration': 4.842}, {'end': 3433.907, 'text': "So I'm back.", 'start': 3432.987, 'duration': 0.92}], 'summary': 'Processing 10,000 photos on a laptop takes about 45 minutes.', 'duration': 29.271, 'max_score': 3404.636, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs3404636.jpg'}], 'start': 2152.711, 'title': 'Implementing neural networks for image classification', 'summary': 'Covers implementing a self-identifying door using python 3.6 and keras, basics of setting up a convolutional neural network (cnn) for image classification, the neural network training process, and preparing image data for training, with emphasis on relevant technologies, functions, and dataset details.', 'chapters': [{'end': 2458.284, 'start': 2152.711, 'title': 'Implementing self-identifying door using python 3.6', 'summary': 'Discusses the implementation of a self-identifying door using python 3.6 and keras, emphasizing the relevance of python version, the keras environment, and the ease of adding layers to the neural network.', 'duration': 305.573, 'highlights': ['The implementation of a self-identifying door is discussed, emphasizing the relevance of Python version and the Keras environment. ', 'The ease of adding layers to the neural network in Keras is highlighted, allowing for quick experimentation with different setups and data. ', 'The importance of specifying the Python version, in this case Python 3.6, for module compatibility is emphasized. ']}, {'end': 3008.094, 'start': 2458.704, 'title': 'Convolutional neural network basics', 'summary': "Explains the basics of setting up a convolutional neural network (cnn) for image classification, involving layers such as convolution 2d, max pooling 2d, flatten, dense, and their respective functions and significance, as well as the use of the relu activation function and the optimizer atom, with an emphasis on ensuring proper input shape matching and the categorization of classes into 'cat' or 'dog'.", 'duration': 549.39, 'highlights': ['The chapter explains the basics of setting up a Convolutional Neural Network (CNN) for image classification, involving layers such as convolution 2D, max pooling 2D, flatten, dense, and their respective functions and significance. The chapter provides an overview of the essential layers involved in setting up a Convolutional Neural Network (CNN) for image classification, including convolution 2D, max pooling 2D, flatten, and dense layers.', 'The use of the RELU activation function in the CNN is emphasized due to its speed and efficiency in producing output based on the input weights. The chapter highlights the significance of using the RELU activation function in the CNN due to its rapid computation of output based on input weights.', 'The importance of ensuring proper input shape matching is emphasized, as errors may occur if the input shape does not match the data being processed. It is emphasized that ensuring proper input shape matching is crucial, as errors may arise if the input shape does not align with the data being processed.', "The categorization of classes into 'cat' or 'dog' is outlined as the objective of the classification process, with the use of a classifier neural network to achieve this distinction. The chapter outlines the objective of classifying images into 'cat' or 'dog' categories, with the use of a classifier neural network to achieve this distinction.", 'The use of the optimizer atom in reverse propagation for training the CNN is explained, with atom being the commonly used optimizer for large data sets. The explanation of using the optimizer atom in reverse propagation for training the CNN is provided, highlighting its common usage for large data sets.']}, {'end': 3181.993, 'start': 3008.274, 'title': 'Neural network training process', 'summary': 'Explains the process of building a neural network, including adding layers, flattening and downsizing data, compiling the layers, and training using separate training and testing sets, along with using keras for preprocessing images and data augmentation.', 'duration': 173.719, 'highlights': ['The process of building a neural network involves adding layers, flattening and downsizing data, and compiling the layers. The neural network is built by adding layers, utilizing activation functions like RELU, flattening and downsizing data to 128, and finally compiling the layers for training.', "The usage of separate training and testing data sets for training the neural network is emphasized. Separate training and testing data sets are used to train and evaluate the neural network's performance, ensuring the model's effectiveness is assessed.", 'The utilization of Keras for preprocessing images and data augmentation is highlighted. Keras is used for preprocessing images, including reshaping data, rescaling, and applying data augmentation techniques like shear range and zoom range.']}, {'end': 3445.553, 'start': 3182.153, 'title': 'Preparing image data for training', 'summary': 'Explains the process of preparing image data for training a neural network, including creating training and test sets, with 800 images in the training set and 2000 images in the test set. the training process involves going through the entire dataset 25 times with 8000 steps per epoch to train the neural network.', 'duration': 263.4, 'highlights': ['Creating the training set with 800 images using the image data generator and setting the target size to 64x64. The process involves creating the training set with 800 images using the image data generator and setting the target size to 64x64.', 'Creating the test set with 2000 images using the image data generator and rescaling the images to 1 over 255. The test set is created with 2000 images using the image data generator and rescaling the images to 1 over 255.', 'Training the neural network involves going through the entire dataset 25 times with 8000 steps per epoch. The training process involves going through the entire dataset 25 times with 8000 steps per epoch to train the neural network.']}], 'duration': 1292.842, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs2152711.jpg', 'highlights': ['The process involves creating the training set with 800 images using the image data generator and setting the target size to 64x64.', 'The test set is created with 2000 images using the image data generator and rescaling the images to 1 over 255.', 'The training process involves going through the entire dataset 25 times with 8000 steps per epoch to train the neural network.', 'Keras is used for preprocessing images, including reshaping data, rescaling, and applying data augmentation techniques like shear range and zoom range.', "Separate training and testing data sets are used to train and evaluate the neural network's performance, ensuring the model's effectiveness is assessed.", 'The neural network is built by adding layers, utilizing activation functions like RELU, flattening and downsizing data to 128, and finally compiling the layers for training.', 'The explanation of using the optimizer atom in reverse propagation for training the CNN is provided, highlighting its common usage for large data sets.', "The chapter outlines the objective of classifying images into 'cat' or 'dog' categories, with the use of a classifier neural network to achieve this distinction.", 'The importance of ensuring proper input shape matching is crucial, as errors may arise if the input shape does not align with the data being processed.', 'The use of the RELU activation function in the CNN is emphasized due to its speed and efficiency in producing output based on the input weights.', 'The chapter provides an overview of the essential layers involved in setting up a Convolutional Neural Network (CNN) for image classification, including convolution 2D, max pooling 2D, flatten, and dense layers.', 'The ease of adding layers to the neural network in Keras is highlighted, allowing for quick experimentation with different setups and data.', 'The implementation of a self-identifying door is discussed, emphasizing the relevance of Python version and the Keras environment.', 'The importance of specifying the Python version, in this case Python 3.6, for module compatibility is emphasized.']}, {'end': 3953.834, 'segs': [{'end': 3800.92, 'src': 'embed', 'start': 3769.452, 'weight': 0, 'content': [{'end': 3771.052, 'text': 'let me get my drawing tool back on.', 'start': 3769.452, 'duration': 1.6}, {'end': 3772.192, 'text': "so let's take a look at this.", 'start': 3771.052, 'duration': 1.14}, {'end': 3778.974, 'text': "we have our test image, we're loading and in here we have test image one and this one hasn't data, hasn't seen this one at all.", 'start': 3772.192, 'duration': 6.782}, {'end': 3779.834, 'text': 'so this is all new.', 'start': 3778.974, 'duration': 0.86}, {'end': 3782.155, 'text': 'oh, let me shrink the screen down, let me start that over.', 'start': 3779.834, 'duration': 2.321}, {'end': 3788.277, 'text': 'So here we have my test image and we went ahead and the cross processing has this nice image setup.', 'start': 3782.535, 'duration': 5.742}, {'end': 3793.438, 'text': "So we're going to load the image and we're going to alter it to 64 by 64 print.", 'start': 3788.337, 'duration': 5.101}, {'end': 3796.059, 'text': "So, right off the bat, we're going to cross this nice.", 'start': 3793.738, 'duration': 2.321}, {'end': 3800.92, 'text': "that way it automatically sets it up for us, so we don't have to redo all our images and find a way to reset those.", 'start': 3796.059, 'duration': 4.861}], 'summary': 'Test image altered to 64x64 print using cross processing setup.', 'duration': 31.468, 'max_score': 3769.452, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs3769452.jpg'}, {'end': 3953.834, 'src': 'embed', 'start': 3918.064, 'weight': 1, 'content': [{'end': 3923.946, 'text': 'And so we have successfully built a neural network that could distinguish between photos of a cat and a dog.', 'start': 3918.064, 'duration': 5.882}, {'end': 3926.247, 'text': 'Imagine all the other things you could distinguish.', 'start': 3924.306, 'duration': 1.941}, {'end': 3931.809, 'text': 'Imagine all the different industries you could dive into with that, just being able to understand those two different pictures.', 'start': 3926.547, 'duration': 5.262}, {'end': 3932.87, 'text': 'What about mosquitoes??', 'start': 3931.929, 'duration': 0.941}, {'end': 3936.671, 'text': 'Could you find the mosquitoes that bite versus the mosquitoes that are friendly?', 'start': 3933.41, 'duration': 3.261}, {'end': 3940.731, 'text': 'It turns out the mosquitoes that bite us are only 4% of the mosquito population.', 'start': 3936.811, 'duration': 3.92}, {'end': 3941.532, 'text': 'if even that, maybe 2%.', 'start': 3940.731, 'duration': 0.801}, {'end': 3949.693, 'text': "There's all kinds of industries that use this, and there's so many industries that are just now realizing how powerful these tools are.", 'start': 3941.532, 'duration': 8.161}, {'end': 3953.834, 'text': 'Just in the photos alone, there is a myriad of industries sprouting up.', 'start': 3949.813, 'duration': 4.021}], 'summary': 'A neural network distinguishes between cat and dog photos, opening opportunities in various industries with powerful tools.', 'duration': 35.77, 'max_score': 3918.064, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs3918064.jpg'}], 'start': 3445.893, 'title': 'Adjusting neural network parameters and neural network for cat and dog classification', 'summary': 'Covers adjustments to neural network parameters, involving changes in steps per epoch, epochs, and validation steps, resulting in reduced processing time. additionally, it discusses building a neural network for cat and dog classification, achieving an 86% accuracy on test data, and highlighting potential industry applications of image classification.', 'chapters': [{'end': 3726.569, 'start': 3445.893, 'title': 'Adjusting neural network parameters', 'summary': 'Covers the adjustments made to the neural network parameters, including changing steps per epoch from 8,000 to 4,000, epochs from 25 to 10, and validation steps from 2,000 to 10, resulting in reduced processing time and the impact of validation steps on accuracy and bias.', 'duration': 280.676, 'highlights': ['Changed steps per epoch from 8,000 to 4,000 and epochs from 25 to 10 The adjustment led to a significant reduction in processing time and increased efficiency.', 'Impact of validation steps on accuracy and bias The explanation of how the reduction in validation steps to 10 affects the accuracy and bias of the neural network, providing insights into the importance of these parameters.', 'Explanation of the impact of adjusting parameters on processing time The speaker illustrates the relationship between the adjustments made and the resulting processing time, emphasizing the trade-offs between processing speed and accuracy.']}, {'end': 3953.834, 'start': 3728.311, 'title': 'Neural network for cat and dog classification', 'summary': 'Discusses building a neural network to classify cat and dog images, achieving an accuracy of 86% on test data and highlighting the potential applications of image classification in various industries.', 'duration': 225.523, 'highlights': ['The neural network achieved an accuracy of 86% on test data. The chapter mentions that when the neural network was run on the server, it achieved an accuracy of about 86% on the test data.', 'The process successfully labeled a dog as a dog and a cat as a cat based on the images. The chapter illustrates how the neural network accurately labeled a dog as a dog and a cat as a cat based on the images, demonstrating the effectiveness of the classification process.', 'The potential applications of image classification in various industries are highlighted. The chapter emphasizes the potential applications of image classification in diverse industries, such as distinguishing between different types of mosquitoes and the emergence of numerous industries utilizing image classification tools.']}], 'duration': 507.941, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs3445893.jpg', 'highlights': ['Changed steps per epoch from 8,000 to 4,000 and epochs from 25 to 10, resulting in reduced processing time and increased efficiency.', 'Neural network achieved an accuracy of 86% on test data, effectively labeling dogs and cats based on images.', 'Explanation of the impact of adjusting parameters on processing time, emphasizing the trade-offs between processing speed and accuracy.', 'The potential applications of image classification in various industries are highlighted, showcasing its diverse uses.']}, {'end': 4909.209, 'segs': [{'end': 4000.471, 'src': 'embed', 'start': 3974.146, 'weight': 2, 'content': [{'end': 3979.03, 'text': "In this case we'll look at the letter A written out on a 28 by 28 pixels.", 'start': 3974.146, 'duration': 4.884}, {'end': 3982.113, 'text': 'So the handwritten alphabets are presented as images of 28 by 28 pixels.', 'start': 3979.35, 'duration': 2.763}, {'end': 3985.115, 'text': 'And that image comes in.', 'start': 3983.934, 'duration': 1.181}, {'end': 3986.877, 'text': 'In this case we have 784 neurons.', 'start': 3985.135, 'duration': 1.742}, {'end': 3989.239, 'text': "That's 28 times 28.", 'start': 3987.117, 'duration': 2.122}, {'end': 3993.724, 'text': 'And the initial prediction is made using random weights assigned to each channel.', 'start': 3989.239, 'duration': 4.485}, {'end': 3996.246, 'text': 'And so we have our forward propagation as you see here.', 'start': 3993.844, 'duration': 2.402}, {'end': 4000.471, 'text': 'So each node is then, their values are added up and added up and so on going across.', 'start': 3996.507, 'duration': 3.964}], 'summary': "Analyzing handwritten 'a' image with 28x28 pixels and 784 neurons for prediction.", 'duration': 26.325, 'max_score': 3974.146, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs3974146.jpg'}, {'end': 4109.734, 'src': 'embed', 'start': 4081.435, 'weight': 1, 'content': [{'end': 4085.919, 'text': '2 equals 12, 3 should come out as 18, and 4 as 24.', 'start': 4081.435, 'duration': 4.484}, {'end': 4089.302, 'text': "And we're just doing multiples of 6 if you take the time to look at it.", 'start': 4085.919, 'duration': 3.383}, {'end': 4093.524, 'text': 'So in our example, we have our input, and it goes into our neural network.', 'start': 4089.562, 'duration': 3.962}, {'end': 4096.026, 'text': 'So this box represents our neural network.', 'start': 4093.624, 'duration': 2.402}, {'end': 4101.749, 'text': "One of the cool things about neural networks is there's always this little black box that you kind of train to do what you want.", 'start': 4096.326, 'duration': 5.423}, {'end': 4104.731, 'text': "And you really don't have to know exactly what the weights are,", 'start': 4101.969, 'duration': 2.762}, {'end': 4109.734, 'text': 'although there are some very high-end setups to start looking at those weights and how they work and what they do.', 'start': 4104.731, 'duration': 5.003}], 'summary': 'Neural network trained to produce multiples of 6 from input numbers', 'duration': 28.299, 'max_score': 4081.435, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs4081435.jpg'}, {'end': 4686.804, 'src': 'embed', 'start': 4657.287, 'weight': 0, 'content': [{'end': 4663.649, 'text': 'So you can plan one of these training neural networks, doing the back propagation to significantly longer,', 'start': 4657.287, 'duration': 6.362}, {'end': 4665.809, 'text': "because you're going over thousands of data points.", 'start': 4663.649, 'duration': 2.16}, {'end': 4674.151, 'text': "And then when you actually run it forward, it's very quick, which makes these things very useful and just really part of today's world in computing.", 'start': 4666.129, 'duration': 8.022}, {'end': 4677.792, 'text': "Today we're going to be covering the convolutional neural network tutorial.", 'start': 4674.311, 'duration': 3.481}, {'end': 4686.804, 'text': 'Do you know how deep learning recognizes the objects in an image? And really, this particular neural network is how image recognition works.', 'start': 4678.459, 'duration': 8.345}], 'summary': 'Training neural networks with back propagation over thousands of data points makes image recognition quick and useful in computing.', 'duration': 29.517, 'max_score': 4657.287, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs4657287.jpg'}, {'end': 4916.832, 'src': 'embed', 'start': 4890.64, 'weight': 3, 'content': [{'end': 4895.082, 'text': 'In this example here, you see flowers of two varieties, orchid and a rose.', 'start': 4890.64, 'duration': 4.442}, {'end': 4899.344, 'text': 'I think the orchid is much more dainty and beautiful and the rose smells quite beautiful.', 'start': 4895.522, 'duration': 3.822}, {'end': 4900.745, 'text': 'I have a couple of rose bushes in my yard.', 'start': 4899.364, 'duration': 1.381}, {'end': 4902.326, 'text': 'They go into the input layer.', 'start': 4901.145, 'duration': 1.181}, {'end': 4909.209, 'text': 'That data is then sent to all the different nodes in the next layer, one of the hidden layers, based on its different weights and its setup.', 'start': 4902.506, 'duration': 6.703}, {'end': 4911.53, 'text': 'It then comes out and gives those a new value.', 'start': 4909.549, 'duration': 1.981}, {'end': 4916.832, 'text': 'Those values then are multiplied by their weights and go to the next hidden layer and so on.', 'start': 4911.67, 'duration': 5.162}], 'summary': 'Comparison of orchid and rose; data flow through layers in neural network.', 'duration': 26.192, 'max_score': 4890.64, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs4890640.jpg'}], 'start': 3954.034, 'title': 'Neural network training', 'summary': 'Discusses training simple neural networks to recognize handwritten alphabets a, b, and c achieving high accuracy and the concept of loss function, gradient descent, and backpropagation in neural networks, with emphasis on image recognition using convolutional neural networks.', 'chapters': [{'end': 4222.91, 'start': 3954.034, 'title': 'Neural network training', 'summary': 'Discusses training a simple neural network to recognize handwritten alphabets a, b, and c using backpropagation and gradient descent, and then training a neural network to predict outputs given inputs, achieving high accuracy through weight adjustments.', 'duration': 268.876, 'highlights': ['The neural network is trained to recognize handwritten alphabets A, B, and C using backpropagation and gradient descent. The neural network is trained to recognize handwritten alphabets A, B, and C using backpropagation and gradient descent. It must be trained to recognize handwritten alphabets A, B, and C. The handwritten alphabets are presented as images of 28 by 28 pixels.', 'Training the network with multiple inputs until it is able to predict with a high accuracy. The network is trained with multiple inputs until it is able to predict with high accuracy. The network is trained with multiple inputs until it is able to predict with high accuracy.', 'Training a neural network to predict outputs given inputs, achieving high accuracy through weight adjustments. The chapter discusses training a neural network to predict outputs given inputs, achieving high accuracy through weight adjustments. The neural network is trained to predict outputs given inputs, achieving high accuracy through weight adjustments.']}, {'end': 4490.92, 'start': 4223.11, 'title': 'Gradient descent and backpropagation in neural networks', 'summary': 'Discusses the concept of loss function, applying it to different input values, using gradient descent to minimize loss, and employing backpropagation to update weights for reducing prediction error in neural networks.', 'duration': 267.81, 'highlights': ['Backpropagation is a process of updating weights of the network in order to reduce the error in prediction. Backpropagation is a crucial process for updating network weights to minimize prediction error.', 'Using gradient descent to minimize loss by finding the minimal of a function graphically and adjusting weight based on slope. Illustrates the process of using gradient descent to minimize loss by visually finding the minimal point of a function and adjusting weight based on the slope.', 'Applying the loss function to input values and calculating the loss for different weights, demonstrating the impact on loss. Demonstrates the impact of different weights on loss by applying the loss function to input values and calculating the resulting loss.']}, {'end': 4909.209, 'start': 4491.5, 'title': 'Backpropagation and convolutional neural networks', 'summary': 'Discusses the iterative process of backpropagation to adjust weights and minimize loss in prediction, emphasizing the significance of training neural networks for image recognition using convolutional neural networks.', 'duration': 417.709, 'highlights': ['The process involves multiple iterations of backpropagation to adjust weights and minimize loss in prediction, leading to accurate predictions. The weights are adjusted through several iterations to minimize the loss in prediction, exemplifying the iterative nature of backpropagation.', 'Backpropagation involves going over thousands of data points to train neural networks, which contrasts the quick predicting process. The training process for neural networks, involving backpropagation, is significantly longer due to processing thousands of data points, in contrast to the swift prediction process.', 'The chapter introduces convolutional neural networks, emphasizing their role in image recognition and the layered process of feature extraction and pattern detection. The introduction of convolutional neural networks highlights their significance in image recognition and emphasizes the process of feature extraction and pattern detection through different layers.']}], 'duration': 955.175, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs3954034.jpg', 'highlights': ['The chapter introduces convolutional neural networks, emphasizing their role in image recognition and the layered process of feature extraction and pattern detection.', 'The neural network is trained to recognize handwritten alphabets A, B, and C using backpropagation and gradient descent. The handwritten alphabets are presented as images of 28 by 28 pixels.', 'Training a neural network to predict outputs given inputs, achieving high accuracy through weight adjustments.', 'Backpropagation is a process of updating weights of the network in order to reduce the error in prediction.', 'Using gradient descent to minimize loss by finding the minimal of a function graphically and adjusting weight based on slope.', 'The process involves multiple iterations of backpropagation to adjust weights and minimize loss in prediction, leading to accurate predictions.']}, {'end': 6004.924, 'segs': [{'end': 5028.422, 'src': 'embed', 'start': 4983.243, 'weight': 5, 'content': [{'end': 4989.067, 'text': "And then we'll bring that back together and see what that looks like when we put the pieces for the convolutional operation.", 'start': 4983.243, 'duration': 5.824}, {'end': 4990.748, 'text': "Here we've set up two arrays.", 'start': 4989.427, 'duration': 1.321}, {'end': 4994.41, 'text': 'We have, in this case, a single dimension matrix.', 'start': 4991.108, 'duration': 3.302}, {'end': 4998.027, 'text': 'And we have A equals 5, 3, 7, 5, 9, 7.', 'start': 4994.63, 'duration': 3.397}, {'end': 5000.233, 'text': 'And we have B equals 1, 2, 3.', 'start': 4998.032, 'duration': 2.201}, {'end': 5003.456, 'text': "So in the convolution, as it comes in there, it's going to look at these two.", 'start': 5000.234, 'duration': 3.222}, {'end': 5005.817, 'text': "And we're going to start by multiplying them.", 'start': 5003.656, 'duration': 2.161}, {'end': 5006.778, 'text': 'A times B.', 'start': 5006.017, 'duration': 0.761}, {'end': 5020.261, 'text': 'And so we multiply the arrays element-wise and we get 5, 6, 6, where 5 is the 5 times 1, 6 is 3 times 2, and then the other 6 is 2 times 3.', 'start': 5007.438, 'duration': 12.823}, {'end': 5024.862, 'text': "And since the two arrays aren't the same size, they're not the same setup,", 'start': 5020.261, 'duration': 4.601}, {'end': 5028.422, 'text': "we're going to just truncate the first one and we're going to look at the second array,", 'start': 5024.862, 'duration': 3.56}], 'summary': 'Explaining convolutional operation using example arrays and element-wise multiplication.', 'duration': 45.179, 'max_score': 4983.243, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs4983243.jpg'}, {'end': 5115.253, 'src': 'embed', 'start': 5089.071, 'weight': 3, 'content': [{'end': 5095.818, 'text': "Now, in a little bit, we're going to cover where we use this math at, this multiplying of matrices and how that works.", 'start': 5089.071, 'duration': 6.747}, {'end': 5103.704, 'text': "But it's important to understand that we're going through the matrix and multiplying the different parts to it to match the smaller matrix with the larger matrix.", 'start': 5096.478, 'duration': 7.226}, {'end': 5109.488, 'text': "I know a lot of people get lost at is, you know, what's going on here with these matrices? Oh, scary math.", 'start': 5104.084, 'duration': 5.404}, {'end': 5111.51, 'text': 'Not really that scary when you break it down.', 'start': 5109.749, 'duration': 1.761}, {'end': 5115.253, 'text': "We're looking at a section of A and we're comparing it to B.", 'start': 5111.61, 'duration': 3.643}], 'summary': 'Explaining matrix multiplication using smaller and larger matrices to match parts, making it less scary for understanding.', 'duration': 26.182, 'max_score': 5089.071, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs5089071.jpg'}, {'end': 5724.168, 'src': 'embed', 'start': 5692.379, 'weight': 2, 'content': [{'end': 5694.581, 'text': 'That generates multiple convolution layers.', 'start': 5692.379, 'duration': 2.202}, {'end': 5699.726, 'text': 'So we have a number of convolution layers we have set up in there, just looking at that data.', 'start': 5694.842, 'duration': 4.884}, {'end': 5705.031, 'text': 'We then take those convolution layers, we run them through the relu setup and then,', 'start': 5699.826, 'duration': 5.205}, {'end': 5709.795, 'text': "once we've done through the relu setup and we have multiple relus going on, multiple layers at our ReLU.", 'start': 5705.031, 'duration': 4.764}, {'end': 5712.438, 'text': "Then we're going to take those multiple layers and we're going to be pooling them.", 'start': 5709.855, 'duration': 2.583}, {'end': 5715.68, 'text': 'So now we have the pooling layers or multiple poolings going on.', 'start': 5712.558, 'duration': 3.122}, {'end': 5719.323, 'text': "Up until this point we're dealing with, sometimes it's multiple dimensions.", 'start': 5715.981, 'duration': 3.342}, {'end': 5724.168, 'text': "You can have three dimensions, some strange data setups that aren't doing images but looking at other things.", 'start': 5719.343, 'duration': 4.825}], 'summary': 'The process involves multiple convolution layers, relu setup, and pooling, with consideration for multiple dimensions and various data setups.', 'duration': 31.789, 'max_score': 5692.379, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs5692379.jpg'}, {'end': 5847.868, 'src': 'embed', 'start': 5820.089, 'weight': 0, 'content': [{'end': 5826.274, 'text': 'Once you get to that step, you might be looking at that going, boy, that looks like the normal Intuit to most neural network.', 'start': 5820.089, 'duration': 6.185}, {'end': 5827.496, 'text': "And you're correct, it is.", 'start': 5826.395, 'duration': 1.101}, {'end': 5832.66, 'text': 'So once we have the flattened matrix from the pooling layer, that becomes our input.', 'start': 5828.096, 'duration': 4.564}, {'end': 5838.465, 'text': 'So the pooling layer is fed as an input to the fully connected layer to classify the image.', 'start': 5833, 'duration': 5.465}, {'end': 5840.747, 'text': 'And so you can see as our flattened matrix comes in.', 'start': 5838.705, 'duration': 2.042}, {'end': 5847.868, 'text': 'In this case we have the pixels from the flattened matrix fed as an input back to our toucan or whatever that kind of bird that is.', 'start': 5841.107, 'duration': 6.761}], 'summary': 'The flattened matrix from the pooling layer is used as input for classification in a neural network.', 'duration': 27.779, 'max_score': 5820.089, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs5820089.jpg'}, {'end': 5951.596, 'src': 'embed', 'start': 5923.169, 'weight': 1, 'content': [{'end': 5926.813, 'text': 'And then Based on that, it uses the ReLU for the pooling.', 'start': 5923.169, 'duration': 3.644}, {'end': 5929.857, 'text': "The pooling, then find out which one's the best, and so on,", 'start': 5926.893, 'duration': 2.964}, {'end': 5934.343, 'text': 'all the way to the fully connected layer at the end or the classification and the output layer.', 'start': 5929.857, 'duration': 4.486}, {'end': 5936.686, 'text': "So that'd be a classification neural network at the end.", 'start': 5934.483, 'duration': 2.203}, {'end': 5939.49, 'text': 'So we covered a lot of theory up till now.', 'start': 5937.207, 'duration': 2.283}, {'end': 5943.292, 'text': 'And you can imagine each one of these steps has to be broken down in code.', 'start': 5939.65, 'duration': 3.642}, {'end': 5947.034, 'text': 'So putting that together can be a little complicated.', 'start': 5943.472, 'duration': 3.562}, {'end': 5951.596, 'text': 'Not that each step of the process is overly complicated, but because we have so many steps.', 'start': 5947.294, 'duration': 4.302}], 'summary': 'A neural network with relu pooling leading to a classification network, which involves many steps.', 'duration': 28.427, 'max_score': 5923.169, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs5923169.jpg'}, {'end': 6013.535, 'src': 'embed', 'start': 5986.629, 'weight': 4, 'content': [{'end': 5991.531, 'text': "And if you're looking at anything in the news at all of our automated cars and everything else,", 'start': 5986.629, 'duration': 4.902}, {'end': 5996.933, 'text': "you can see where this kind of processing is so important in today's world and very cutting edge.", 'start': 5991.531, 'duration': 5.402}, {'end': 6000.097, 'text': "as far as what's coming out in the commercial deployment.", 'start': 5997.313, 'duration': 2.784}, {'end': 6001.759, 'text': 'I mean, this is really cool stuff.', 'start': 6000.378, 'duration': 1.381}, {'end': 6004.924, 'text': "We're starting to see this just about everywhere in industry.", 'start': 6001.84, 'duration': 3.084}, {'end': 6008.208, 'text': 'So a great time to be playing with this and figuring it all out.', 'start': 6005.344, 'duration': 2.864}, {'end': 6013.535, 'text': "Let's go ahead and dive into the code and see what that looks like when we're actually writing our script.", 'start': 6008.469, 'duration': 5.066}], 'summary': 'Automated cars and other industries are leveraging cutting-edge processing, making it a great time to explore and understand this technology.', 'duration': 26.906, 'max_score': 5986.629, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs5986629.jpg'}], 'start': 4909.549, 'title': 'Understanding convolutional neural networks and cnn structure & image classification', 'summary': 'Covers the convolutional operation, functioning of convolutional layers, and cnn structure for image recognition, involving pixel arrays, matrix multiplication, filters, relu layer, pooling, flattening, and classification, with a practical application using the cifar-10 dataset.', 'chapters': [{'end': 5251.017, 'start': 4909.549, 'title': 'Understanding convolutional neural networks', 'summary': 'Explains the convolutional operation in cnn, involving the representation of images as pixel arrays, matrix multiplication in the convolutional operation, and the layers in a convolutional neural network.', 'duration': 341.468, 'highlights': ['Convolutional operation forms the basis of any convolutional neural network, representing images as arrays of pixel values. This explains the fundamental role of the convolutional operation in CNN and the representation of images as arrays of pixel values.', 'Explanation of matrix multiplication in the convolutional operation involving two matrices A and B, demonstrating element-wise multiplication and sum of products. The detailed explanation of matrix multiplication in the convolutional operation, clarifying the process of element-wise multiplication and sum of products.', 'Description of layers in a convolutional neural network, including the convolution layer, ReLU layer, pooling layer, and fully connected layer. This provides an overview of the different layers involved in a convolutional neural network, outlining their specific functions in processing images.']}, {'end': 5617.101, 'start': 5251.477, 'title': 'Understanding convolutional neural networks', 'summary': 'Explains the functioning of convolutional layers in a convolutional neural network, including the use of filters, dot product computation, feature extraction, relu layer, forward propagation, and pooling to reduce dimensionality, aiming to identify specific features in the input images.', 'duration': 365.624, 'highlights': ['A convolution layer has a number of filters and performs convolution operation on images considered as a matrix of pixel values. The convolution layer in a convolutional neural network includes multiple filters and performs convolution operations on images represented as matrices of pixel values.', 'Filters are derived during programming or model training and are used to detect various image patterns, such as edges and different parts. Filters are derived during programming or model training and are utilized to detect various image patterns, such as edges and different parts, by sliding over the image and computing dot products to detect patterns.', 'The ReLU layer introduces non-linearity to the network by setting negative pixels to zero and performs an element-wise operation. The ReLU layer introduces non-linearity to the network by setting negative pixels to zero and performing an element-wise operation, leading to the extraction of feature maps from the convolved images.', "Pooling is a downsampling operation that reduces the dimensionality of the feature map, aiming to simplify and manage the data. Pooling is a downsampling operation that reduces the dimensionality of the feature map, aiming to simplify and manage the data by reducing the rectified feature map's size through max pooling with specific filters and stride values."]}, {'end': 6004.924, 'start': 5617.101, 'title': 'Cnn structure & image classification', 'summary': 'Covers the structure and functioning of a convolutional neural network (cnn) for image recognition, including the process of filtering, pooling, flattening, and classification, with a practical application using the cifar-10 dataset for classifying images across 10 categories.', 'duration': 387.823, 'highlights': ['The process of filtering, pooling, flattening, and classification in a Convolutional Neural Network (CNN) is explained, with a practical application using the CIFAR-10 dataset for classifying images across 10 categories. Explanation of the CNN process; Practical application using the CIFAR-10 dataset; Image classification across 10 categories.', 'Pooling layer uses different filters to identify different parts of the image, such as edges, corners, body, feathers, eyes, and beak. Pooling layer function; Identifying different parts of the image.', 'Flattening is a process of converting all of the resultant two-dimensional arrays from pooled feature map into a single long continuous linear vector. Explanation of the flattening process; Conversion of two-dimensional arrays into a linear vector.', 'The process of filtering, pooling, flattening, and classification in a Convolutional Neural Network (CNN) is crucial in the practical application using the CIFAR-10 dataset for classifying images across 10 categories. Importance of CNN process in practical application; CIFAR-10 dataset usage for image classification.', 'The CNN structure and functioning for image recognition is explained, including the process of filtering, pooling, flattening, and classification. Explanation of CNN structure and functioning; Image recognition process.']}], 'duration': 1095.375, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs4909549.jpg', 'highlights': ['Explanation of the CNN process; Practical application using the CIFAR-10 dataset; Image classification across 10 categories.', 'The CNN structure and functioning for image recognition is explained, including the process of filtering, pooling, flattening, and classification.', 'Filters are derived during programming or model training and are used to detect various image patterns, such as edges and different parts.', 'The process of filtering, pooling, flattening, and classification in a Convolutional Neural Network (CNN) is explained, with a practical application using the CIFAR-10 dataset for classifying images across 10 categories.', 'A convolution layer has a number of filters and performs convolution operation on images considered as a matrix of pixel values.', 'The ReLU layer introduces non-linearity to the network by setting negative pixels to zero and performs an element-wise operation.', 'Pooling is a downsampling operation that reduces the dimensionality of the feature map, aiming to simplify and manage the data.', 'Convolutional operation forms the basis of any convolutional neural network, representing images as arrays of pixel values.', 'Description of layers in a convolutional neural network, including the convolution layer, ReLU layer, pooling layer, and fully connected layer.', 'Flattening is a process of converting all of the resultant two-dimensional arrays from pooled feature map into a single long continuous linear vector.']}, {'end': 6908.68, 'segs': [{'end': 6792.567, 'src': 'embed', 'start': 6750.029, 'weight': 0, 'content': [{'end': 6756.211, 'text': 'And you also remember that we had the number of photos, in this case, length of test or whatever number is in there.', 'start': 6750.029, 'duration': 6.182}, {'end': 6761.453, 'text': 'We also have 32 by 32 by 3.', 'start': 6756.572, 'duration': 4.881}, {'end': 6770.257, 'text': "So when we're looking at the batch size, we want to change this from 10, 000 to a batch of, in this case, I think we're going to do batches of 100.", 'start': 6761.453, 'duration': 8.804}, {'end': 6772.298, 'text': 'So we want to look at just 100, the first 100 of the photos.', 'start': 6770.257, 'duration': 2.041}, {'end': 6777.42, 'text': 'And if you remember, we set selfi equal to 0.', 'start': 6773.718, 'duration': 3.702}, {'end': 6781.122, 'text': "So what we're looking at here is we're going to create x.", 'start': 6777.42, 'duration': 3.702}, {'end': 6783.643, 'text': "We're going to get the next batch from the very initialized.", 'start': 6781.122, 'duration': 2.521}, {'end': 6785.384, 'text': "We've already initialized it for 0.", 'start': 6783.663, 'duration': 1.721}, {'end': 6790.847, 'text': "So we're going to look at x from 0 to batch size, which we've set to 100.", 'start': 6785.384, 'duration': 5.463}, {'end': 6792.567, 'text': 'So just the first 100 images.', 'start': 6790.847, 'duration': 1.72}], 'summary': 'Adjusting batch size from 10,000 to 100 images for analysis.', 'duration': 42.538, 'max_score': 6750.029, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs6750029.jpg'}], 'start': 6005.344, 'title': 'Data processing and visualization in machine learning', 'summary': 'Covers using matplotlib in jupyter notebook for data visualization, reshaping 10,000 images into a 10,000x3x32x32 array, and setting up self-training images and labels, emphasizing the importance of data manipulation for machine learning models.', 'chapters': [{'end': 6084.647, 'start': 6005.344, 'title': 'Data visualization with matplotlib', 'summary': 'Discusses using matplotlib in jupyter notebook to display and visualize data batch keys and images, and reshape the data, highlighting the importance of matplotlib for visualization and the use of jupyter notebook for interactive display of variables.', 'duration': 79.303, 'highlights': ['The use of Matplotlib for displaying images and reshaping data in Jupyter Notebook is emphasized, showcasing its importance in data visualization.', 'The capability of Jupyter Notebook to display variables without the need for print statements is highlighted, enhancing the interactive nature of the platform.', 'The breakdown of data batch one keys into batch one label, data, and file names is presented, providing insight into the structure of the dataset.', 'The process of importing and setting up Matplotlib for visualizing data in Jupyter Notebook is explained, demonstrating the practical implementation of the library for data visualization.']}, {'end': 6547.644, 'start': 6084.967, 'title': 'Data reshaping and neural network model preparation', 'summary': 'Discusses reshaping 10,000 images into a 10,000x3x32x32 array, the importance of using integer 8 data type for memory efficiency, and the creation of helper functions for processing the data and preparing for the neural network model.', 'duration': 462.677, 'highlights': ['Reshaping 10,000 images into a 10,000x3x32x32 array The data is reshaped from a single line of bit data into a 10,000x3x32x32 array, representing 10,000 images with color channels and dimensions of 32x32.', 'Importance of using integer 8 data type for memory efficiency Using integer 8 data type is crucial for memory efficiency, as it prevents excessive RAM usage compared to float data types. This is essential for handling large datasets.', 'Creation of helper functions for processing the data and preparing for the neural network model The chapter describes the creation of helper functions, including a one hot encoder for labels and a class CIFAR helper to set up and initialize training and testing data for the neural network model preparation.']}, {'end': 6908.68, 'start': 6548.085, 'title': 'Setting up self-training images and labels', 'summary': 'Explains the process of setting up self-training images and labels, including loading the data, reshaping images, one hot encoding labels, and creating batches, to prepare them for the machine learning model, with a focus on batch processing and data format manipulation.', 'duration': 360.595, 'highlights': ['The chapter explains the process of creating batches for handling large amounts of image data, emphasizing the importance of breaking up the data into smaller batch sizes, such as 100 images, and reshaping the data into the correct format for processing.', 'The process of loading and formatting the self-training images and labels is outlined, including stacking the images into a NumPy array, obtaining the training length, reshaping the images into the required dimensions, and converting the labels into an array of 10 values using one hot encoding.', 'The step-by-step procedure for setting up the test images and labels is detailed, which involves stacking the images, obtaining the length of images, reshaping and transposing the data, and applying one hot encoding to the labels, ensuring that the test images are in the same format as the training images.', 'The importance of breaking up the data into batches for efficient processing is highlighted, with a focus on obtaining a smaller batch size, reshaping the data, and incrementing the batch index to access the next set of images and labels.']}], 'duration': 903.336, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs6005344.jpg', 'highlights': ['Reshaping 10,000 images into a 10,000x3x32x32 array, representing 10,000 images with color channels and dimensions of 32x32.', 'The process of creating batches for handling large amounts of image data, emphasizing the importance of breaking up the data into smaller batch sizes, such as 100 images, and reshaping the data into the correct format for processing.', 'The process of loading and formatting the self-training images and labels, including stacking the images into a NumPy array, obtaining the training length, reshaping the images into the required dimensions, and converting the labels into an array of 10 values using one hot encoding.', 'The capability of Jupyter Notebook to display variables without the need for print statements is highlighted, enhancing the interactive nature of the platform.']}, {'end': 8788.767, 'segs': [{'end': 6940.133, 'src': 'embed', 'start': 6908.68, 'weight': 2, 'content': [{'end': 6913.422, 'text': "batch equals ch, next batch of 100, because we're going to use the 100 size.", 'start': 6908.68, 'duration': 4.742}, {'end': 6914.823, 'text': "But we'll come back to that.", 'start': 6913.883, 'duration': 0.94}, {'end': 6915.443, 'text': "We're going to use that.", 'start': 6914.843, 'duration': 0.6}, {'end': 6919.746, 'text': "Just remember that that's part of our code we're going to be using in a minute from the definition we just made.", 'start': 6915.463, 'duration': 4.283}, {'end': 6921.767, 'text': "So now we're ready to create our model.", 'start': 6920.006, 'duration': 1.761}, {'end': 6925.308, 'text': 'First thing we want to do is we want to import our tensorflow as tf.', 'start': 6921.847, 'duration': 3.461}, {'end': 6927.208, 'text': "I'll just go ahead and run that so it's loaded up.", 'start': 6925.488, 'duration': 1.72}, {'end': 6930.089, 'text': 'And you can see we got a warning here.', 'start': 6927.709, 'duration': 2.38}, {'end': 6932.29, 'text': "That's because they're making some changes.", 'start': 6930.83, 'duration': 1.46}, {'end': 6933.25, 'text': "It's always growing.", 'start': 6932.35, 'duration': 0.9}, {'end': 6940.133, 'text': "And they're going to be depreciating one of the values from float64 to float type or is treated as an NP float64.", 'start': 6933.471, 'duration': 6.662}], 'summary': 'Preparing to create a model with tensorflow, encountering a warning due to changes in float type.', 'duration': 31.453, 'max_score': 6908.68, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs6908680.jpg'}, {'end': 6987.381, 'src': 'embed', 'start': 6961.059, 'weight': 1, 'content': [{'end': 6965.661, 'text': "It doesn't matter which one it goes through, so the depreciation would not affect our code as we have it.", 'start': 6961.059, 'duration': 4.602}, {'end': 6973.848, 'text': "And in our tensorflow, we'll go ahead, let me just increase the size in there just a moment so you can get a better view of what we're typing in.", 'start': 6966.181, 'duration': 7.667}, {'end': 6975.97, 'text': "We're going to set a couple placeholders here.", 'start': 6974.229, 'duration': 1.741}, {'end': 6981.575, 'text': "And so we're going to set x equals tfplaceholder tffloat32.", 'start': 6976.27, 'duration': 5.305}, {'end': 6985.179, 'text': 'We just talked about the float64 versus the numpy float.', 'start': 6981.755, 'duration': 3.424}, {'end': 6987.381, 'text': "We're actually just going to keep this at float32.", 'start': 6985.259, 'duration': 2.122}], 'summary': 'Using tensorflow, setting placeholders with float32 data type.', 'duration': 26.322, 'max_score': 6961.059, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs6961059.jpg'}, {'end': 7143.461, 'src': 'embed', 'start': 7114.947, 'weight': 9, 'content': [{'end': 7120.59, 'text': "So we're going to go ahead and just init some random numbers based on the shape with a standard deviation of 0.1.", 'start': 7114.947, 'duration': 5.643}, {'end': 7121.81, 'text': 'Kind of a fun way to do that.', 'start': 7120.59, 'duration': 1.22}, {'end': 7125.693, 'text': 'And then the tf variable, init random distribution.', 'start': 7122.131, 'duration': 3.562}, {'end': 7127.994, 'text': "So we're just creating a random distribution on there.", 'start': 7125.853, 'duration': 2.141}, {'end': 7129.395, 'text': "That's all that is for the weights.", 'start': 7128.074, 'duration': 1.321}, {'end': 7131.316, 'text': 'Now, you might change that.', 'start': 7129.835, 'duration': 1.481}, {'end': 7133.457, 'text': 'You might have a higher standard deviation.', 'start': 7131.356, 'duration': 2.101}, {'end': 7136.418, 'text': 'In some cases, you actually load preset weights.', 'start': 7133.737, 'duration': 2.681}, {'end': 7137.638, 'text': "That's pretty rare.", 'start': 7136.758, 'duration': 0.88}, {'end': 7143.461, 'text': "Usually, you're testing that against another model or something like that, and you want to see how those weights configure with each other.", 'start': 7138.099, 'duration': 5.362}], 'summary': 'Initializing random numbers based on shape with a 0.1 standard deviation, creating a random distribution for weights, potentially loading preset weights for testing against another model.', 'duration': 28.514, 'max_score': 7114.947, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs7114947.jpg'}, {'end': 7316.868, 'src': 'embed', 'start': 7257.439, 'weight': 4, 'content': [{'end': 7260.881, 'text': 'So after each time we run it through the convectional layer, we want to pool the data.', 'start': 7257.439, 'duration': 3.442}, {'end': 7265.564, 'text': 'If you remember correctly on the pool side, and let me just get rid of all my marks.', 'start': 7261.321, 'duration': 4.243}, {'end': 7266.724, 'text': "It's getting a little crazy there.", 'start': 7265.644, 'duration': 1.08}, {'end': 7269.446, 'text': "And in fact, let's go ahead and jump back to that slide.", 'start': 7267.064, 'duration': 2.382}, {'end': 7271.367, 'text': "Let's just take a look at that slide over here.", 'start': 7269.526, 'duration': 1.841}, {'end': 7273.79, 'text': 'So we have our image coming in.', 'start': 7272.228, 'duration': 1.562}, {'end': 7276.754, 'text': 'We create our convolutional layer with all the filters.', 'start': 7274.03, 'duration': 2.724}, {'end': 7277.635, 'text': 'Remember the filters.', 'start': 7276.814, 'duration': 0.821}, {'end': 7283.803, 'text': "go, you know the filters coming in here, and it looks at these four boxes and then, if it's a step, let's say step two,", 'start': 7277.635, 'duration': 6.168}, {'end': 7287.067, 'text': 'it then goes to these four boxes and then the next step, and so on.', 'start': 7283.803, 'duration': 3.264}, {'end': 7291.471, 'text': 'So we have our convolutional layer that we generate, or convolutional layers.', 'start': 7287.547, 'duration': 3.924}, {'end': 7294.354, 'text': 'They use the relu function.', 'start': 7291.711, 'duration': 2.643}, {'end': 7296.016, 'text': "There's other functions out there.", 'start': 7294.755, 'duration': 1.261}, {'end': 7301.241, 'text': 'For this, though, the relu is the one that works the best, at least so far.', 'start': 7296.156, 'duration': 5.085}, {'end': 7302.342, 'text': "I'm sure that will change.", 'start': 7301.281, 'duration': 1.061}, {'end': 7303.663, 'text': 'Then we have our pooling.', 'start': 7302.622, 'duration': 1.041}, {'end': 7306.066, 'text': 'Now, if you remember correctly, the pooling was max.', 'start': 7303.764, 'duration': 2.302}, {'end': 7315.708, 'text': 'So if we had the filter coming in and they did the multiplication on there and we have a 1 and maybe a 2 here and another 1 here and a 3 here,', 'start': 7307.006, 'duration': 8.702}, {'end': 7316.868, 'text': '3 is the max.', 'start': 7315.708, 'duration': 1.16}], 'summary': 'Using convolutional layers and pooling to process image data, applying relu function and max pooling for best results.', 'duration': 59.429, 'max_score': 7257.439, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs7257439.jpg'}, {'end': 7516.814, 'src': 'embed', 'start': 7486.138, 'weight': 13, 'content': [{'end': 7493.181, 'text': 'So notice to look at the first model and set the data accordingly, set that up So it matches.', 'start': 7486.138, 'duration': 7.043}, {'end': 7495.862, 'text': 'And we went ahead and ran this already.', 'start': 7493.781, 'duration': 2.081}, {'end': 7496.503, 'text': 'I think I ran it.', 'start': 7496.042, 'duration': 0.461}, {'end': 7497.403, 'text': 'Let me go ahead and run it again.', 'start': 7496.523, 'duration': 0.88}, {'end': 7500.525, 'text': "And if we're going to do one layer, let's go ahead and do a second layer down here.", 'start': 7497.523, 'duration': 3.002}, {'end': 7503.487, 'text': "And we'll call it Convo 2.", 'start': 7500.545, 'duration': 2.942}, {'end': 7505.308, 'text': "It's also a convolutional layer on this.", 'start': 7503.487, 'duration': 1.821}, {'end': 7509.05, 'text': "And you'll see that we're feeding Convolutional 1 in, the pooling.", 'start': 7505.488, 'duration': 3.562}, {'end': 7516.814, 'text': 'So it goes from Convolutional 1 into Convolutional 1 pooling, from Convolutional 1 pooling into Convolutional 2,', 'start': 7509.19, 'duration': 7.624}], 'summary': 'Adjusting model layers and running it again for better performance.', 'duration': 30.676, 'max_score': 7486.138, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs7486138.jpg'}, {'end': 7551.287, 'src': 'embed', 'start': 7525.557, 'weight': 12, 'content': [{'end': 7536.36, 'text': "And for our flattened layer, let's go ahead and we'll do, since we have 64 coming out of here and we have 4 by 4 going in, let's do 8 by 8 by 64.", 'start': 7525.557, 'duration': 10.803}, {'end': 7538.32, 'text': "So let's do 4096.", 'start': 7536.36, 'duration': 1.96}, {'end': 7541.601, 'text': 'This is going to be the flat layer.', 'start': 7538.32, 'duration': 3.281}, {'end': 7544.382, 'text': "So that's how many bits are coming through on the flat layer.", 'start': 7541.661, 'duration': 2.721}, {'end': 7545.763, 'text': "And we'll reshape this.", 'start': 7544.542, 'duration': 1.221}, {'end': 7548.525, 'text': "So we'll reshape our Convo 2 pooling.", 'start': 7545.803, 'duration': 2.722}, {'end': 7551.287, 'text': 'And that will feed into here, the Convo 2 pooling.', 'start': 7548.785, 'duration': 2.502}], 'summary': 'The flattened layer will have 4096 bits coming through.', 'duration': 25.73, 'max_score': 7525.557, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs7525557.jpg'}, {'end': 7810.823, 'src': 'embed', 'start': 7790.168, 'weight': 6, 'content': [{'end': 7801.236, 'text': 'the cross entropy between two probability distributions over the same underlying set of events measures the average number of bits needed to identify an event drawn from the set.', 'start': 7790.168, 'duration': 11.068}, {'end': 7802.397, 'text': "That's a mouthful.", 'start': 7801.516, 'duration': 0.881}, {'end': 7805.599, 'text': "Really we're just looking at the amount of error in here.", 'start': 7802.957, 'duration': 2.642}, {'end': 7809.522, 'text': 'How many of these are correct and how many of these are incorrect.', 'start': 7805.839, 'duration': 3.683}, {'end': 7810.823, 'text': 'So how much of it matches.', 'start': 7809.682, 'duration': 1.141}], 'summary': 'Cross entropy measures error in identifying events, determining correctness and matching.', 'duration': 20.655, 'max_score': 7790.168, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs7790168.jpg'}, {'end': 7872.318, 'src': 'embed', 'start': 7845.21, 'weight': 3, 'content': [{'end': 7848.911, 'text': 'So our optimizer is going to equal the TF train atom optimizer.', 'start': 7845.21, 'duration': 3.701}, {'end': 7853.152, 'text': "If you don't remember what the learning rate is, let me just pop this back into here.", 'start': 7849.091, 'duration': 4.061}, {'end': 7854.173, 'text': "Here's our learning rate.", 'start': 7853.272, 'duration': 0.901}, {'end': 7858.974, 'text': 'When you have your weights, you have all your weights and your different nodes that are coming out.', 'start': 7854.593, 'duration': 4.381}, {'end': 7860.955, 'text': "Here's our node coming out.", 'start': 7859.034, 'duration': 1.921}, {'end': 7861.935, 'text': 'It has all its weights.', 'start': 7860.995, 'duration': 0.94}, {'end': 7868.357, 'text': 'And then the error is being sent back through in reverse on our neural network.', 'start': 7862.455, 'duration': 5.902}, {'end': 7872.318, 'text': 'So we take this error and we adjust these weights based on the different formulas.', 'start': 7868.697, 'duration': 3.621}], 'summary': 'Training optimizer adjusts weights based on error in neural network.', 'duration': 27.108, 'max_score': 7845.21, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs7845210.jpg'}, {'end': 8078.889, 'src': 'embed', 'start': 8046.773, 'weight': 15, 'content': [{'end': 8049.454, 'text': "So that's 50, 000 pictures we're going to process right there.", 'start': 8046.773, 'duration': 2.681}, {'end': 8056.476, 'text': "In the first process, we're going to do a session run.", 'start': 8054.235, 'duration': 2.241}, {'end': 8057.577, 'text': "We're going to take our train.", 'start': 8056.496, 'duration': 1.081}, {'end': 8060.418, 'text': 'We created our train variable or optimizer in there.', 'start': 8057.817, 'duration': 2.601}, {'end': 8061.939, 'text': "We're going to feed it the dictionary.", 'start': 8060.478, 'duration': 1.461}, {'end': 8069.862, 'text': 'We had our feed dictionary that we created, and we have x equals batch 0 coming in, y true, batch 1.', 'start': 8062.319, 'duration': 7.543}, {'end': 8071.063, 'text': 'Hold the probability, 0.5.', 'start': 8069.862, 'duration': 1.201}, {'end': 8078.889, 'text': "And then just so that we can keep track of what's going on, every 100 steps we're going to run a print.", 'start': 8071.063, 'duration': 7.826}], 'summary': 'Processing 50,000 pictures, running session, feeding dictionary, tracking progress every 100 steps.', 'duration': 32.116, 'max_score': 8046.773, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs8046773.jpg'}, {'end': 8701.42, 'src': 'embed', 'start': 8675.134, 'weight': 0, 'content': [{'end': 8681.78, 'text': "So if you have a series of things, and because three points back affects what's happening now and what's your output affects what's happening.", 'start': 8675.134, 'duration': 6.646}, {'end': 8682.581, 'text': "that's very important.", 'start': 8681.78, 'duration': 0.801}, {'end': 8685.023, 'text': 'So whatever I put as an output is going to affect the next one.', 'start': 8682.621, 'duration': 2.402}, {'end': 8687.105, 'text': "A feedforward doesn't look at any of that.", 'start': 8685.204, 'duration': 1.901}, {'end': 8688.727, 'text': "It just looks at this is what's coming in.", 'start': 8687.145, 'duration': 1.582}, {'end': 8690.689, 'text': 'And it cannot memorize previous inputs.', 'start': 8688.827, 'duration': 1.862}, {'end': 8693.111, 'text': "So it doesn't have that list of inputs coming in.", 'start': 8690.949, 'duration': 2.162}, {'end': 8695.053, 'text': 'Solution to feedforward neural network.', 'start': 8693.331, 'duration': 1.722}, {'end': 8701.42, 'text': "You'll see here where it says recurrent neural network, and we have our X on the bottom going to H going to Y.", 'start': 8695.553, 'duration': 5.867}], 'summary': 'Recurrent neural network can memorize previous inputs and affects the next one, unlike feedforward network.', 'duration': 26.286, 'max_score': 8675.134, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs8675134.jpg'}, {'end': 8736.581, 'src': 'embed', 'start': 8709.668, 'weight': 10, 'content': [{'end': 8713.853, 'text': 'And the hidden layers, as they produce data, feed into the next one.', 'start': 8709.668, 'duration': 4.185}, {'end': 8721.756, 'text': 'So your hidden layer might have an output that goes off to y, but that output goes back into the next prediction coming in.', 'start': 8713.993, 'duration': 7.763}, {'end': 8724.837, 'text': 'What this does is this allows it to handle sequential data.', 'start': 8721.796, 'duration': 3.041}, {'end': 8728.818, 'text': 'It considers the current input and also the previously received inputs.', 'start': 8724.997, 'duration': 3.821}, {'end': 8735.38, 'text': "And if we're going to look at general drawings and solutions, we should also look at applications of the RNN.", 'start': 8729.218, 'duration': 6.162}, {'end': 8736.581, 'text': 'Image captioning.', 'start': 8735.68, 'duration': 0.901}], 'summary': "Rnn's hidden layers handle sequential data and enable image captioning.", 'duration': 26.913, 'max_score': 8709.668, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs8709668.jpg'}], 'start': 6908.68, 'title': 'Tensorflow model training', 'summary': 'Covers setting up a tensorflow model training session with 50,000 pictures processed in batches of 100, achieving an initial accuracy of 0.1028 and improving to 0.39 after running the full training.', 'chapters': [{'end': 7273.79, 'start': 6908.68, 'title': 'Creating tensorflow model and helper functions', 'summary': 'Outlines the process of creating a tensorflow model, including the use of placeholders for input data and dropout probability, and the definition of helper functions for weight and bias initialization, convolutional and pooling layers.', 'duration': 365.11, 'highlights': ['The chapter discusses the process of creating a TensorFlow model, including the use of placeholders for input data and dropout probability. Creation of TensorFlow model, use of input data placeholders, inclusion of dropout probability', 'The definition of helper functions for weight and bias initialization, convolutional and pooling layers is explained in detail. Explanation of helper functions, weight and bias initialization, convolutional and pooling layers']}, {'end': 7708.514, 'start': 7274.03, 'title': 'Convolutional neural network basics', 'summary': "Discusses the basics of creating a convolutional neural network, including the use of convolutional layers, pooling, flattening, and fully connected layers, with a focus on key parameters and their impact on the network's architecture and performance.", 'duration': 434.484, 'highlights': ['The process of creating convolutional layers and their parameters, including filters, stride, and input size. The chapter provides detailed insights into creating convolutional layers, specifying the importance of parameters such as filters, stride, and input size.', 'The utilization of max pooling and its impact on data reduction and reshaping. The chapter highlights the use of max pooling for data reduction and reshaping, emphasizing its role in the network architecture.', 'The transformation of data into a single array through flattening and its significance in the overall network structure. The process of flattening the data into a single array is described, emphasizing its significance in the overall network structure.', 'The creation of fully connected layers and the considerations for defining the layer size and activation functions. Insights into creating fully connected layers, including considerations for defining layer size and activation functions, are provided.', "The application of dropout and the role of loss function and optimizer in training the network. The chapter discusses the application of dropout, the role of loss function, and the optimizer in training the network, emphasizing their importance in enhancing the network's performance."]}, {'end': 7900.031, 'start': 7708.914, 'title': 'Optimizing model with loss function', 'summary': 'Discusses the impact of changing numbers in a model, the importance of exponential growth in number selection, the creation of a loss function, and the use of an atom optimizer in model training.', 'duration': 191.117, 'highlights': ['The importance of exponential growth in number selection Using exponential growth (e.g., 2, 4, 8, 16) is recommended for number selection to achieve optimal results in the model.', 'Creation of a cross entropy loss function The chapter introduces the concept of cross entropy loss function, which measures the average error between true and predicted probability distributions.', 'Use of atom optimizer in model training The use of an atom optimizer is emphasized for adjusting model weights, with a small shift (0.001) to prevent bias towards the last data sent through.', "Impact of changing numbers in a model Changing numbers in the model can significantly affect the model's outcome and fit, with potential trade-offs between better fit and resource usage."]}, {'end': 8198.513, 'start': 7900.251, 'title': 'Tensorflow model training', 'summary': 'Covers setting up a tensorflow model training session with 50,000 pictures processed in batches of 100, achieving an initial accuracy of 0.1028 and improving to 0.39 after running the full training.', 'duration': 298.262, 'highlights': ['50,000 pictures processed in batches of 100 The model processes 50,000 pictures in batches of 100 during the training session.', 'Initial accuracy of 0.1028 The initial accuracy of the model during training is 0.1028.', 'Improved accuracy of 0.39 after full training The accuracy of the model improves to 0.39 after running the full training.']}, {'end': 8481.658, 'start': 8198.893, 'title': 'Understanding neural networks and rnns', 'summary': 'Discusses the interpretation of accuracy and loss in neural networks, emphasizing the significance of achieving an accuracy over 0.5, with 0.95 indicating 100%, and provides practical insights on using recurrent neural networks (rnns) for predictive text analysis and the implementation of long short term memory (lstm) in keras on tensorflow.', 'duration': 282.765, 'highlights': ['Neural network accuracy and loss interpretation The significance of achieving an accuracy over 0.5, with 0.95 indicating 100%, and the interpretation of loss as a logarithmic scale, emphasizing the rarity of 1 accuracy and 0 loss.', 'Recurrent Neural Networks (RNNs) and practical use case Insights on the working of RNNs for predictive text analysis, using a recurrent neural network to predict the next word in a sentence, and the practical implementation of long short term memory (LSTM) in Keras on TensorFlow.', "Application of RNNs in predictive text analysis Explanation of how Google's autocomplete feature uses a recurrent neural network to predict the next word in a sentence and the practical utility of the feature in saving time during internet searches."]}, {'end': 8788.767, 'start': 8481.658, 'title': 'Understanding neural networks and rnn', 'summary': 'Discusses the basics of neural networks, including feed-forward and recurrent neural networks, and their applications, emphasizing the need for rnn to handle sequential data and its applications in image captioning and time series prediction.', 'duration': 307.109, 'highlights': ['The chapter discusses the basics of neural networks, including feed-forward and recurrent neural networks It explains that neural networks consist of different layers connected to each other and work on the structure and functions of a human brain, with recurrent neural networks being essential for handling sequential data.', 'The need for RNN to handle sequential data and its applications in image captioning and time series prediction It highlights the significance of recurrent neural networks in analyzing sequential data and mentions its applications in image captioning, such as analyzing a dog catching a ball in midair, and in time series prediction for stock prices.', 'The limitations of feedforward neural networks and the solution provided by recurrent neural networks It explains the limitations of feedforward neural networks in handling sequential data and the solution provided by recurrent neural networks, which consider both the current and previously received inputs, making them suitable for sequential data analysis.']}], 'duration': 1880.087, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs6908680.jpg', 'highlights': ['The model processes 50,000 pictures in batches of 100 during the training session.', 'The accuracy of the model improves to 0.39 after running the full training.', 'The chapter discusses the process of creating a TensorFlow model, including the use of placeholders for input data and dropout probability.', 'The definition of helper functions for weight and bias initialization, convolutional and pooling layers is explained in detail.', 'The process of creating convolutional layers and their parameters, including filters, stride, and input size.', 'The utilization of max pooling for data reduction and reshaping, emphasizing its role in the network architecture.', 'The transformation of data into a single array through flattening and its significance in the overall network structure.', 'The creation of fully connected layers and the considerations for defining the layer size and activation functions.', "The application of dropout, the role of loss function, and the optimizer in training the network, emphasizing their importance in enhancing the network's performance.", 'The importance of exponential growth in number selection for achieving optimal results in the model.', 'Creation of a cross entropy loss function, which measures the average error between true and predicted probability distributions.', 'Use of atom optimizer in model training, emphasized for adjusting model weights with a small shift (0.001) to prevent bias towards the last data sent through.', "Changing numbers in the model can significantly affect the model's outcome and fit, with potential trade-offs between better fit and resource usage.", 'The significance of achieving an accuracy over 0.5, with 0.95 indicating 100%, and the interpretation of loss as a logarithmic scale, emphasizing the rarity of 1 accuracy and 0 loss.', 'Insights on the working of RNNs for predictive text analysis, using a recurrent neural network to predict the next word in a sentence, and the practical implementation of long short term memory (LSTM) in Keras on TensorFlow.', "Explanation of how Google's autocomplete feature uses a recurrent neural network to predict the next word in a sentence and the practical utility of the feature in saving time during internet searches.", 'The chapter discusses the basics of neural networks, including feed-forward and recurrent neural networks, and the significance of recurrent neural networks in handling sequential data.', 'The need for RNN to handle sequential data and its applications in image captioning and time series prediction, highlighting the significance of recurrent neural networks in analyzing sequential data and mentioning its applications in image captioning and time series prediction for stock prices.', 'The limitations of feedforward neural networks in handling sequential data and the solution provided by recurrent neural networks, which consider both the current and previously received inputs, making them suitable for sequential data analysis.']}, {'end': 9852.99, 'segs': [{'end': 8968.38, 'src': 'embed', 'start': 8942.33, 'weight': 2, 'content': [{'end': 8947.032, 'text': "Going from left to right, you'll see that the C goes in, and then the X goes in.", 'start': 8942.33, 'duration': 4.702}, {'end': 8950.113, 'text': 'So the X is going upward bound, and C is going to the right.', 'start': 8947.332, 'duration': 2.781}, {'end': 8953.334, 'text': 'A is going out, and C is also going out.', 'start': 8950.593, 'duration': 2.741}, {'end': 8954.455, 'text': "That's where it gets a little confusing.", 'start': 8953.374, 'duration': 1.081}, {'end': 8962.238, 'text': 'So here we have X in, C in, and then we have Y out, and C out, and C is based on HTN.', 'start': 8954.515, 'duration': 7.723}, {'end': 8964.338, 'text': 'minus 1.', 'start': 8963.238, 'duration': 1.1}, {'end': 8968.38, 'text': 'So our value is based on the y and the h value are connected to each other.', 'start': 8964.338, 'duration': 4.042}], 'summary': 'X goes in, c goes in, a goes out, c goes out based on htn, with a value connected to y and h.', 'duration': 26.05, 'max_score': 8942.33, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs8942330.jpg'}, {'end': 9116.25, 'src': 'embed', 'start': 9087.31, 'weight': 10, 'content': [{'end': 9094.056, 'text': "One of the biggest things you need to understand when we're working with this neural network is what's called the vanishing gradient problem.", 'start': 9087.31, 'duration': 6.746}, {'end': 9100.803, 'text': 'While training an RNN, your slope can be either too small or very large, and this makes training difficult.', 'start': 9094.277, 'duration': 6.526}, {'end': 9105.147, 'text': 'When the slope is too small, the problem is known as vanishing gradient.', 'start': 9100.923, 'duration': 4.224}, {'end': 9109.928, 'text': "And you'll see here, they have a nice image, loss of information through time.", 'start': 9105.427, 'duration': 4.501}, {'end': 9116.25, 'text': "So if you're pushing not enough information forward, that information is lost and then, when you go to train it,", 'start': 9110.168, 'duration': 6.082}], 'summary': 'Neural network faces vanishing gradient problem, causing loss of information during rnn training.', 'duration': 28.94, 'max_score': 9087.31, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs9087310.jpg'}, {'end': 9319.194, 'src': 'embed', 'start': 9293.334, 'weight': 0, 'content': [{'end': 9298.019, 'text': "And then finally, there's long short-term memory networks, the LSTMS.", 'start': 9293.334, 'duration': 4.685}, {'end': 9300.001, 'text': 'And we can make adjustments to that.', 'start': 9298.419, 'duration': 1.582}, {'end': 9305.466, 'text': 'So just like we can clip the gradient as it comes out, we can also expand on that.', 'start': 9300.101, 'duration': 5.365}, {'end': 9309.771, 'text': 'We can increase the memory network, the size of it, so it handles more information.', 'start': 9305.607, 'duration': 4.164}, {'end': 9316.593, 'text': "And one of the most common problems in today's setup is what they call long-term dependencies.", 'start': 9310.111, 'duration': 6.482}, {'end': 9319.194, 'text': 'Suppose we try to predict the last word in the text.', 'start': 9316.953, 'duration': 2.241}], 'summary': 'Lstm networks can be adjusted to handle long-term dependencies and increase memory network size.', 'duration': 25.86, 'max_score': 9293.334, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs9293334.jpg'}, {'end': 9366.805, 'src': 'embed', 'start': 9336.263, 'weight': 4, 'content': [{'end': 9337.643, 'text': 'No, you probably said Spanish.', 'start': 9336.263, 'duration': 1.38}, {'end': 9341.486, 'text': 'The word we predict will depend on the previous few words in context.', 'start': 9338.004, 'duration': 3.482}, {'end': 9345.188, 'text': 'Here we need the context of Spain to predict the last word in the text.', 'start': 9341.726, 'duration': 3.462}, {'end': 9351.092, 'text': "It's possible that the gap between the relevant information and the point where it is needed to become very large.", 'start': 9345.508, 'duration': 5.584}, {'end': 9354.414, 'text': 'LSTMs help us solve this problem.', 'start': 9351.472, 'duration': 2.942}, {'end': 9362.081, 'text': 'So the LSTMs are a special kind of recurrent neural network capable of learning long-term dependencies.', 'start': 9354.574, 'duration': 7.507}, {'end': 9366.805, 'text': 'Remembering information for long periods of time is their default behavior.', 'start': 9362.301, 'duration': 4.504}], 'summary': 'Lstms solve long-term dependency problem in context prediction.', 'duration': 30.542, 'max_score': 9336.263, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs9336263.jpg'}, {'end': 9649.805, 'src': 'embed', 'start': 9618.545, 'weight': 1, 'content': [{'end': 9623.392, 'text': 'So step two is then to decide how much should this unit add to the current state.', 'start': 9618.545, 'duration': 4.847}, {'end': 9628.996, 'text': 'in the second layer there are two parts one is the sigmoid function and the other is the tangent h.', 'start': 9623.792, 'duration': 5.204}, {'end': 9632.558, 'text': 'in the sigmoid function it decides which values to let through.', 'start': 9628.996, 'duration': 3.562}, {'end': 9639.823, 'text': 'zero or one tangent h function gives the weightage to the values which are passed, deciding their level of importance minus one to one.', 'start': 9632.558, 'duration': 7.265}, {'end': 9649.805, 'text': 'And you can see the two formulators that come up the i of t equals the sigmoid of the weight of i, h of t minus 1, x of t, plus the bias of i.', 'start': 9640.063, 'duration': 9.742}], 'summary': "Decide unit's impact, using sigmoid & tangent h functions, to control values & their importance.", 'duration': 31.26, 'max_score': 9618.545, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs9618545.jpg'}, {'end': 9703.017, 'src': 'embed', 'start': 9664.049, 'weight': 5, 'content': [{'end': 9669.63, 'text': "If this seems a little complicated, don't worry because a lot of the programming is already done when we get to the case study.", 'start': 9664.049, 'duration': 5.581}, {'end': 9675.955, 'text': "Understanding, though, that this is part of the program is important when you're trying to figure out what to set your settings at.", 'start': 9669.93, 'duration': 6.025}, {'end': 9682.14, 'text': "You should also note, when you're looking at this, it should have some semblance to your forward propagation neural networks,", 'start': 9676.275, 'duration': 5.865}, {'end': 9686.303, 'text': 'where we have a value assigned to a weight plus a bias.', 'start': 9682.14, 'duration': 4.163}, {'end': 9689.846, 'text': 'Very important steps in any of the neural network layers,', 'start': 9686.784, 'duration': 3.062}, {'end': 9697.232, 'text': "whether we're propagating into them the information from one to the next or we're just doing a straightforward neural network propagation.", 'start': 9689.846, 'duration': 7.386}, {'end': 9703.017, 'text': "Let's take a quick look at this, what it looks like from the human standpoint, as I step out of my suit again.", 'start': 9697.472, 'duration': 5.545}], 'summary': 'Understanding the programming is important in setting settings for neural networks.', 'duration': 38.968, 'max_score': 9664.049, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs9664049.jpg'}, {'end': 9808.658, 'src': 'embed', 'start': 9782.206, 'weight': 6, 'content': [{'end': 9790.451, 'text': 'Then we put the cell state through the tangent h to push the values to be between minus one and one and multiply it by the output of the sigmoid gate.', 'start': 9782.206, 'duration': 8.245}, {'end': 9799.775, 'text': 'So, when we talk about the output of t, we set that equal to the sigmoid of the weight of 0 of the h of t minus 1,', 'start': 9790.911, 'duration': 8.864}, {'end': 9803.436, 'text': 'back one step in time by the x of t, plus, of course, the bias.', 'start': 9799.775, 'duration': 3.661}, {'end': 9808.658, 'text': 'The h of t equals the out of t times the tangent h of c of t.', 'start': 9803.676, 'duration': 4.982}], 'summary': 'Lstm cell state is processed through tangent h and sigmoid gate for output.', 'duration': 26.452, 'max_score': 9782.206, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs9782206.jpg'}], 'start': 8789.027, 'title': 'Rnn applications and principles', 'summary': 'Discusses applications of rnn in language processing such as text mining, sentiment analysis, and machine translation. it also covers the concept of recurrent neural networks, types, gradient problems, and solutions, emphasizing lstm networks and their processing steps.', 'chapters': [{'end': 8887.512, 'start': 8789.027, 'title': 'Rnn applications in language processing', 'summary': 'Discusses the applications of recurrent neural networks (rnn) in natural language processing, including text mining, sentiment analysis, and machine translation, highlighting the impact of word order on sentiment analysis and the importance of accurate translation.', 'duration': 98.485, 'highlights': ['RNN is used for natural language processing, including text mining and sentiment analysis, which can be crucial due to the impact of word order on sentiment analysis.', 'RNN can be utilized for machine translation, ensuring accurate word order and translation across different languages.', 'The importance of word order in sentiment analysis is emphasized, showcasing how the order of words can significantly alter the sentiment of a sentence.', 'The chapter also mentions the various languages into which English can be translated using RNN, such as Chinese, Italian, French, German, and Spanish.']}, {'end': 9086.626, 'start': 8887.772, 'title': 'Recurrent neural networks', 'summary': 'Explains the concept of recurrent neural networks, the principle of saving the output of a layer and feeding it back to the input, and covers types of recurrent neural networks including one-to-one, one-to-many, many-to-one, and many-to-many, with examples and applications.', 'duration': 198.854, 'highlights': ['Recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. This explains the fundamental principle of recurrent neural networks, emphasizing the feedback mechanism for predicting the output.', 'Types of recurrent neural networks include one-to-one, one-to-many, many-to-one, and many-to-many, with examples such as image captioning and machine translation. This covers the various types of recurrent neural networks and provides examples to illustrate their applications.', 'One-to-one neural network is usually known as a vanilla neural network used for regular machine learning problems. This highlights the common type of recurrent neural network and its association with regular machine learning problems.', 'Many-to-many networks take in a sequence of inputs and generate a sequence of outputs, such as machine translation. This emphasizes the complexity and capability of many-to-many recurrent neural networks, specifically in the context of machine translation.']}, {'end': 9417.547, 'start': 9087.31, 'title': 'Rnn gradient problems and solutions', 'summary': 'Discusses the vanishing and exploding gradient problems in rnns, presenting issues such as loss of information, memory errors, and difficulty predicting next words, and proposes solutions including identity initialization, gradient clipping, and lstms to handle long-term dependencies.', 'duration': 330.237, 'highlights': ['LSTMs as a solution to handle long-term dependencies LSTMs help solve the problem of predicting next words by learning long-term dependencies, capable of remembering information for long periods of time.', 'Exploding gradient problem and its solutions The chapter addresses the issues caused by the exploding gradient problem, including long tracking time, poor performance, and memory errors, proposing solutions such as identity initialization, gradient clipping, and truncating back propagation.', 'Vanishing gradient problem and its solutions The vanishing gradient problem leads to loss of information, making it difficult to predict next words, and the solutions include weight initialization, choosing the right activation function, and using LSTMs to handle long-term dependencies.']}, {'end': 9852.99, 'start': 9417.847, 'title': 'Understanding lstm and its processing steps', 'summary': 'Explains the concept of long short-term memory (lstm) networks and their processing steps, including forgetting irrelevant information, selectively updating cell state values, and outputting certain parts of the cell state, to effectively handle sequential information. it also emphasizes the significance of forget gate, input gate, and output gate in lstm processing.', 'duration': 435.143, 'highlights': ['LSTM involves three processing steps: forgetting irrelevant parts of the previous state, selectively updating cell state values, and outputting certain parts of the cell state. The LSTM network encompasses three essential processing steps: forgetting irrelevant information from the previous state, selectively updating cell state values, and outputting specific parts of the cell state, contributing to effective handling of sequential information.', 'The forget gate in LSTM determines which information to delete from the previous time step, based on the current input, using a sigmoid function. The forget gate in LSTM plays a crucial role in deciding which information to omit from the previous time step, leveraging a sigmoid function to assess the relevance of information based on the current input.', 'The input gate in LSTM analyzes the important information from the current input and selectively updates the cell state values. The input gate within LSTM performs a critical function by analyzing essential information from the current input and selectively updating the cell state values, ensuring relevant information is retained.', 'The output gate in LSTM allows the passed-in information to impact the output in the current time step, contributing to effective sequence prediction. The output gate within LSTM enables the information passed in to influence the output in the current time step, playing a significant role in sequence prediction and decision-making.', 'The processing steps of LSTM network, including forgetting irrelevant information and selectively updating cell state values, aim to effectively manage sequential information, enhancing its applicability in handling complex data. The processing steps of the LSTM network, such as forgetting irrelevant information and selectively updating cell state values, are designed to effectively manage sequential information, thereby enhancing its applicability in handling complex and lengthy data sequences.']}], 'duration': 1063.963, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs8789027.jpg', 'highlights': ['RNN is used for natural language processing, including text mining and sentiment analysis, crucial due to the impact of word order on sentiment analysis.', 'RNN can be utilized for machine translation, ensuring accurate word order and translation across different languages.', 'The importance of word order in sentiment analysis is emphasized, showcasing how the order of words can significantly alter the sentiment of a sentence.', 'The chapter also mentions the various languages into which English can be translated using RNN, such as Chinese, Italian, French, German, and Spanish.', 'Recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer.', 'Types of recurrent neural networks include one-to-one, one-to-many, many-to-one, and many-to-many, with examples such as image captioning and machine translation.', 'LSTMs as a solution to handle long-term dependencies LSTMs help solve the problem of predicting next words by learning long-term dependencies, capable of remembering information for long periods of time.', 'Exploding gradient problem and its solutions The chapter addresses the issues caused by the exploding gradient problem, proposing solutions such as identity initialization, gradient clipping, and truncating back propagation.', 'Vanishing gradient problem and its solutions The vanishing gradient problem leads to loss of information, making it difficult to predict next words, and the solutions include weight initialization, choosing the right activation function, and using LSTMs to handle long-term dependencies.', 'LSTM involves three processing steps: forgetting irrelevant parts of the previous state, selectively updating cell state values, and outputting certain parts of the cell state.', 'The forget gate in LSTM determines which information to delete from the previous time step, based on the current input, using a sigmoid function.', 'The input gate in LSTM analyzes the important information from the current input and selectively updates the cell state values.', 'The output gate in LSTM allows the passed-in information to impact the output in the current time step, contributing to effective sequence prediction.', 'The processing steps of LSTM network, including forgetting irrelevant information and selectively updating cell state values, aim to effectively manage sequential information, enhancing its applicability in handling complex data.']}, {'end': 10813.711, 'segs': [{'end': 10131.502, 'src': 'embed', 'start': 10102.341, 'weight': 5, 'content': [{'end': 10106.626, 'text': "In fact, depending on what your system is, since we're using Keras, I put an overlap here.", 'start': 10102.341, 'duration': 4.285}, {'end': 10112.674, 'text': "But you'll find that almost maybe even half of the code we do is all about the data prep.", 'start': 10107.327, 'duration': 5.347}, {'end': 10118.162, 'text': "And the reason I overlap this with Keras, let me just put that down because that's what we're working in.", 'start': 10113.135, 'duration': 5.027}, {'end': 10121.627, 'text': 'is because Keras has like their own preset stuff.', 'start': 10118.562, 'duration': 3.065}, {'end': 10123.81, 'text': "So it's already pre-built in, which is really nice.", 'start': 10121.647, 'duration': 2.163}, {'end': 10127.135, 'text': "So there's a couple steps a lot of times that are in the Keras setup.", 'start': 10123.93, 'duration': 3.205}, {'end': 10131.502, 'text': "We'll take a look at that to see what comes up in our code as we go through and look at stock.", 'start': 10127.616, 'duration': 3.886}], 'summary': 'Using keras for system, data prep is half the code, keras has pre-built presets.', 'duration': 29.161, 'max_score': 10102.341, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs10102341.jpg'}, {'end': 10272.392, 'src': 'embed', 'start': 10244.86, 'weight': 8, 'content': [{'end': 10247.601, 'text': 'This is the standard stuff that we import into our stock.', 'start': 10244.86, 'duration': 2.741}, {'end': 10250.363, 'text': 'One of the most basic set of information you can look at in stock.', 'start': 10247.682, 'duration': 2.681}, {'end': 10251.683, 'text': "It's all free to download.", 'start': 10250.583, 'duration': 1.1}, {'end': 10254.285, 'text': 'In this case, we downloaded it from Google.', 'start': 10252.044, 'duration': 2.241}, {'end': 10256.025, 'text': "That's why we call it the Google stock price.", 'start': 10254.345, 'duration': 1.68}, {'end': 10258.346, 'text': 'And it specifically is Google.', 'start': 10256.465, 'duration': 1.881}, {'end': 10264.349, 'text': 'This is the Google stock values from, as you can see here, we started off at 1-3-2012.', 'start': 10258.546, 'duration': 5.803}, {'end': 10272.392, 'text': 'So when we look at this first setup up here, we have a data set train equals PD underscore CSV.', 'start': 10265.369, 'duration': 7.023}], 'summary': 'Stock data from google downloaded for analysis since 1-3-2012.', 'duration': 27.532, 'max_score': 10244.86, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs10244860.jpg'}, {'end': 10489.783, 'src': 'embed', 'start': 10425.237, 'weight': 0, 'content': [{'end': 10426.818, 'text': "we're using, uh, multiplication.", 'start': 10425.237, 'duration': 1.581}, {'end': 10432.59, 'text': "so it's going to be minus 5 and then 100 divided, or 95 divided by 1.", 'start': 10427.458, 'duration': 5.132}, {'end': 10436.534, 'text': 'so, or whatever value is, is divided by 95..', 'start': 10432.59, 'duration': 3.944}, {'end': 10441.356, 'text': "And once we've actually created our scale, we're telling it's going to be from 0 to 1.", 'start': 10436.534, 'duration': 4.822}, {'end': 10445.137, 'text': "We want to take our training set, and we're going to create a training set scaled.", 'start': 10441.356, 'duration': 3.781}, {'end': 10448.678, 'text': "And we're going to use our scaler, SC, and we're going to fit.", 'start': 10445.157, 'duration': 3.521}, {'end': 10451.199, 'text': "We're going to fit and transform the training set.", 'start': 10448.738, 'duration': 2.461}, {'end': 10456.94, 'text': "So we can now use the SC, this particular object, we'll use it later on our testing set.", 'start': 10451.879, 'duration': 5.061}, {'end': 10461.602, 'text': 'Because remember, we have to also scale that when we go to test our model and see how it works.', 'start': 10457.12, 'duration': 4.482}, {'end': 10463.304, 'text': "And we'll go ahead and click on the run.", 'start': 10462.122, 'duration': 1.182}, {'end': 10468.03, 'text': "Again, it's not going to have any output yet because we're just setting up all the variables.", 'start': 10463.544, 'duration': 4.486}, {'end': 10475.615, 'text': "Okay, so we paste the data in here, and we're going to create the data structure with the 60 time steps and output.", 'start': 10469.192, 'duration': 6.423}, {'end': 10482.479, 'text': "First note, we're running 60 time steps, and that is where this value here also comes in.", 'start': 10476.336, 'duration': 6.143}, {'end': 10489.783, 'text': 'So the first thing we do is we create our X train and Y train variables, and we set them to an empty Python array.', 'start': 10482.899, 'duration': 6.884}], 'summary': 'Using scaling to prepare training and testing sets for machine learning model.', 'duration': 64.546, 'max_score': 10425.237, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs10425237.jpg'}], 'start': 9853.53, 'title': 'Lstm for stock price prediction', 'summary': 'Discusses training a neural network to use lstm for predicting stock prices, focusing on a case study for 2017 based on 2012-2016 data with a 3 terabytes daily data volume, implementing lstm using python 3.6 in anaconda jupyter notebook, and covering stock price data analysis with python including feature scaling and creating training sets.', 'chapters': [{'end': 9917.242, 'start': 9853.53, 'title': 'Lstm network for stock price prediction', 'summary': 'Discusses the training of a neural network to use lstm for predicting stock prices, focusing on a case study to predict stock prices for 2017 based on 2012-2016 data and highlighting the massive data volume of 3 terabytes generated daily by the new york stock exchange.', 'duration': 63.712, 'highlights': ['The New York Stock Exchange generates roughly 3 terabytes of data per day This highlights the massive data volume of 3 terabytes generated daily by the New York Stock Exchange, emphasizing the enormous amount of data available for analysis.', 'Using LSTM network to predict stock prices for 2017 based on 2012-2016 data This showcases the application of LSTM network for predicting stock prices, indicating a specific use case and the time frame for the prediction.', 'Training a neural network to use LSTM for predicting stock prices This highlights the focus on training a neural network to use LSTM for stock price prediction, emphasizing the practical application of the LSTM network in financial forecasting.']}, {'end': 10222.289, 'start': 9917.242, 'title': 'Implementing lstm for stock price prediction', 'summary': 'Discusses the implementation of lstm for stock price prediction using python 3.6 in anaconda jupyter notebook, emphasizing the data prep, evaluation, and the usage of keras under tensorflow.', 'duration': 305.047, 'highlights': ['The chapter discusses the implementation of LSTM for stock price prediction using Python 3.6 in Anaconda Jupyter notebook It outlines the use of LSTM for stock price prediction and the specific environment being used for implementation.', 'Emphasizes the data prep, evaluation, and the usage of Keras under TensorFlow It highlights the significance of data preparation and evaluation, as well as the utilization of Keras under TensorFlow for the implementation.', 'Pre-processing and data preparation is a crucial part of the implementation, with approximately half of the code dedicated to this phase It emphasizes the importance of pre-processing and data preparation, accounting for almost half of the implementation code.', 'The chapter covers the process of splitting the data into training and testing sets, with 20% of the data earmarked for testing It explains the process of splitting the data into training and testing sets, reserving 20% of the data for testing purposes.']}, {'end': 10813.711, 'start': 10222.93, 'title': 'Stock price analysis with python', 'summary': 'Covers the process of importing and processing stock price data using python, including feature scaling, creating training sets, and preparing data for a lstm model in keras.', 'duration': 590.781, 'highlights': ['The chapter explains the process of importing and processing stock price data using Python, including feature scaling, creating training sets, and preparing data for a LSTM model in Keras. Importing and processing stock price data, feature scaling, creating training sets, preparing data for LSTM model', 'The data consists of comma-separated variables including date, open, high, low, close, and volume, which is the standard information used in stock analysis and can be downloaded for free. Data structure: date, open, high, low, close, volume; Standard information used in stock analysis; Free data download from Google', 'The transcript covers the process of setting up and running Python code for data processing, including changing file paths and using Pandas for data manipulation. Setting up and running Python code, changing file paths, using Pandas for data manipulation']}], 'duration': 960.181, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs9853530.jpg', 'highlights': ['Using LSTM network to predict stock prices for 2017 based on 2012-2016 data', 'The New York Stock Exchange generates roughly 3 terabytes of data per day', 'Training a neural network to use LSTM for predicting stock prices', 'The chapter discusses the implementation of LSTM for stock price prediction using Python 3.6 in Anaconda Jupyter notebook', 'Emphasizes the data prep, evaluation, and the usage of Keras under TensorFlow', 'The chapter covers the process of splitting the data into training and testing sets, with 20% of the data earmarked for testing', 'Pre-processing and data preparation is a crucial part of the implementation, with approximately half of the code dedicated to this phase', 'The chapter explains the process of importing and processing stock price data using Python, including feature scaling, creating training sets, and preparing data for a LSTM model in Keras', 'The data consists of comma-separated variables including date, open, high, low, close, and volume, which is the standard information used in stock analysis and can be downloaded for free']}, {'end': 11836.004, 'segs': [{'end': 10855.155, 'src': 'embed', 'start': 10813.971, 'weight': 4, 'content': [{'end': 10817.515, 'text': 'And I said we were going to jump in and start looking at what those layers mean.', 'start': 10813.971, 'duration': 3.544}, {'end': 10818.155, 'text': 'I meant that.', 'start': 10817.715, 'duration': 0.44}, {'end': 10822.119, 'text': "And we're going to start off with initializing the RNN.", 'start': 10818.355, 'duration': 3.764}, {'end': 10823.981, 'text': "And then we'll start adding those layers in.", 'start': 10822.339, 'duration': 1.642}, {'end': 10827.464, 'text': "And you'll see that we have the LSTM and then the dropout.", 'start': 10824.081, 'duration': 3.383}, {'end': 10829.046, 'text': 'LSTM then dropout.', 'start': 10827.725, 'duration': 1.321}, {'end': 10830.627, 'text': 'LSTM then dropout.', 'start': 10829.306, 'duration': 1.321}, {'end': 10833.791, 'text': "What the heck is that doing? So let's explore that.", 'start': 10830.968, 'duration': 2.823}, {'end': 10839.618, 'text': "We'll start by initializing the RNN, regressor equals sequential, because we're using the sequential model.", 'start': 10833.911, 'duration': 5.707}, {'end': 10841.46, 'text': "And we'll run that and load that up.", 'start': 10839.778, 'duration': 1.682}, {'end': 10846.747, 'text': "And then we're going to start adding our LSTM layer and some dropout regularization.", 'start': 10841.761, 'duration': 4.986}, {'end': 10849.971, 'text': 'And right there should be the Q, dropout regularization.', 'start': 10847.027, 'duration': 2.944}, {'end': 10855.155, 'text': "And if we go back here and remember our exploding gradient, well, that's what we're talking about.", 'start': 10850.191, 'duration': 4.964}], 'summary': 'Initiating rnn, adding lstm and dropout layers, addressing gradient issues', 'duration': 41.184, 'max_score': 10813.971, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs10813971.jpg'}, {'end': 11011.738, 'src': 'embed', 'start': 10982.609, 'weight': 5, 'content': [{'end': 10985.872, 'text': '50 is coming out from our last layer, is coming up, you know,', 'start': 10982.609, 'duration': 3.263}, {'end': 10991.856, 'text': "goes into the regressor and of course we have our dropout and that's what's coming into this one and so on and so on.", 'start': 10985.872, 'duration': 5.984}, {'end': 10995.298, 'text': "and so the next three layers, we don't have to let it know what the shape is.", 'start': 10991.856, 'duration': 3.442}, {'end': 10998.64, 'text': "it automatically understands that and we're going to keep the units the same.", 'start': 10995.298, 'duration': 3.342}, {'end': 11000.381, 'text': "we're still going to do 50 units.", 'start': 10998.64, 'duration': 1.741}, {'end': 11003.904, 'text': "it's still a sequence coming through 50 units and a sequence.", 'start': 11000.381, 'duration': 3.523}, {'end': 11006.934, 'text': 'Now the next piece of code is what brings it all together?', 'start': 11004.332, 'duration': 2.602}, {'end': 11008.415, 'text': "Let's go ahead and take a look at that.", 'start': 11007.014, 'duration': 1.401}, {'end': 11011.738, 'text': 'And we come in here, we put the output layer, the dense layer.', 'start': 11008.655, 'duration': 3.083}], 'summary': 'Neural network has 50 units in sequence, followed by an output dense layer.', 'duration': 29.129, 'max_score': 10982.609, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs10982609.jpg'}, {'end': 11163.208, 'src': 'embed', 'start': 11137.446, 'weight': 3, 'content': [{'end': 11142.632, 'text': 'We have the regressor.fit, xtrain, ytrain, epics, and batch size.', 'start': 11137.446, 'duration': 5.186}, {'end': 11143.973, 'text': 'So we know where this is.', 'start': 11143.092, 'duration': 0.881}, {'end': 11146.535, 'text': 'This is our data coming in for the X train.', 'start': 11144.033, 'duration': 2.502}, {'end': 11150.678, 'text': "Our Y train is the answer we're looking for of our data, our sequential input.", 'start': 11146.575, 'duration': 4.103}, {'end': 11154.801, 'text': "Apex is how many times we're going to go over the whole data set.", 'start': 11150.998, 'duration': 3.803}, {'end': 11156.863, 'text': 'We created a whole data set of X trains.', 'start': 11155.101, 'duration': 1.762}, {'end': 11161.606, 'text': 'So this is each of those rows, which includes a time sequence of 60.', 'start': 11156.923, 'duration': 4.683}, {'end': 11163.208, 'text': 'And batch size.', 'start': 11161.606, 'duration': 1.602}], 'summary': 'Using regressor.fit with xtrain and ytrain data for time sequence of 60, with specified batch size and epochs.', 'duration': 25.762, 'max_score': 11137.446, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs11137446.jpg'}, {'end': 11329.307, 'src': 'embed', 'start': 11303.112, 'weight': 1, 'content': [{'end': 11307.795, 'text': "And we can see we've labeled it part three, making the predictions and visualizing the results.", 'start': 11303.112, 'duration': 4.683}, {'end': 11312.497, 'text': 'So the first thing we need to do is go ahead and read the data in from our test CSV.', 'start': 11308.135, 'duration': 4.362}, {'end': 11315.739, 'text': "You see I've changed the path on it for my computer.", 'start': 11312.777, 'duration': 2.962}, {'end': 11318, 'text': "And then we'll call it the real stock price.", 'start': 11315.759, 'duration': 2.241}, {'end': 11324.144, 'text': "And again, we're doing just the one column here and the values from iLocation.", 'start': 11318.641, 'duration': 5.503}, {'end': 11327.566, 'text': "So it's all the rows and just the values from that one location.", 'start': 11324.264, 'duration': 3.302}, {'end': 11329.307, 'text': "That's the open, stock open.", 'start': 11327.606, 'duration': 1.701}], 'summary': 'Part three: making predictions and visualizing results using stock price data.', 'duration': 26.195, 'max_score': 11303.112, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs11303112.jpg'}, {'end': 11369.141, 'src': 'embed', 'start': 11341.937, 'weight': 7, 'content': [{'end': 11345.24, 'text': "We're going to do a little pandas concat from the data set train.", 'start': 11341.937, 'duration': 3.303}, {'end': 11350.264, 'text': 'Now remember, the end of the data set train is part of the data going in.', 'start': 11345.36, 'duration': 4.904}, {'end': 11352.793, 'text': "let's just visualize that just a little bit.", 'start': 11350.672, 'duration': 2.121}, {'end': 11354.034, 'text': "here's our train data.", 'start': 11352.793, 'duration': 1.241}, {'end': 11357.716, 'text': 'let me just put tr for train, and it went up to this value here.', 'start': 11354.034, 'duration': 3.682}, {'end': 11362.078, 'text': 'but each one of these values generated a bunch of columns.', 'start': 11357.716, 'duration': 4.362}, {'end': 11369.141, 'text': 'it was 60 across, and this value here equals this one, and this value here equals this one, and this value here equals this one.', 'start': 11362.078, 'duration': 7.063}], 'summary': 'Pandas concat on train data with 60 columns generated.', 'duration': 27.204, 'max_score': 11341.937, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs11341937.jpg'}, {'end': 11431.126, 'src': 'embed', 'start': 11401.443, 'weight': 0, 'content': [{'end': 11403.824, 'text': "And then we'll call it the real stock price.", 'start': 11401.443, 'duration': 2.381}, {'end': 11410.03, 'text': "And again, we're doing just the one column here and the values from iLocation.", 'start': 11404.504, 'duration': 5.526}, {'end': 11413.433, 'text': "So it's all the rows and just the values from that one location.", 'start': 11410.13, 'duration': 3.303}, {'end': 11415.175, 'text': "That's the open, stock open.", 'start': 11413.474, 'duration': 1.701}, {'end': 11416.296, 'text': "Let's go ahead and run that.", 'start': 11415.275, 'duration': 1.021}, {'end': 11417.698, 'text': "So that's loaded in there.", 'start': 11416.697, 'duration': 1.001}, {'end': 11420.214, 'text': "And then let's go ahead and create.", 'start': 11417.992, 'duration': 2.222}, {'end': 11421.215, 'text': 'We have our inputs.', 'start': 11420.254, 'duration': 0.961}, {'end': 11422.897, 'text': "We're going to create inputs here.", 'start': 11421.756, 'duration': 1.141}, {'end': 11425.76, 'text': 'And this should all look familiar because this is the same thing we did before.', 'start': 11423.097, 'duration': 2.663}, {'end': 11427.662, 'text': "We're going to take our data set total.", 'start': 11426, 'duration': 1.662}, {'end': 11431.126, 'text': "We're going to do a little pandas concat from the data set train.", 'start': 11427.822, 'duration': 3.304}], 'summary': 'Analyzing stock data using pandas concat and ilocation.', 'duration': 29.683, 'max_score': 11401.443, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs11401443.jpg'}, {'end': 11583.495, 'src': 'embed', 'start': 11557.076, 'weight': 2, 'content': [{'end': 11565.059, 'text': 'And once again we take our X test, we convert it to a numpy array, we do the same reshape we did before, and then we get down to the final two lines,', 'start': 11557.076, 'duration': 7.983}, {'end': 11566.299, 'text': 'and here we have something new.', 'start': 11565.059, 'duration': 1.24}, {'end': 11566.899, 'text': 'right here.', 'start': 11566.579, 'duration': 0.32}, {'end': 11570.583, 'text': 'on these last two lines, let me just highlight those or mark them.', 'start': 11566.899, 'duration': 3.684}, {'end': 11574.907, 'text': 'predicted stock price equals regressor dot predicts x test.', 'start': 11570.583, 'duration': 4.324}, {'end': 11576.508, 'text': "so we're predicting all the stock,", 'start': 11574.907, 'duration': 1.601}, {'end': 11583.495, 'text': 'including both the training and the testing model here and then we want to take this prediction and we want to inverse the transform.', 'start': 11576.508, 'duration': 6.987}], 'summary': 'Using numpy array, reshaping and predicting stock prices for both training and testing models.', 'duration': 26.419, 'max_score': 11557.076, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs11557076.jpg'}], 'start': 10813.971, 'title': 'Building and training rnn models', 'summary': 'Explores initializing rnn, adding lstm layers, and dropout regularization, then discusses building an rnn model with keras, explaining layer composition and training with atom optimizer. it outlines running a neural network model to make predictions on stock prices, emphasizing resource management, data processing, training duration, testing, visualization, and model evaluation.', 'chapters': [{'end': 10964.071, 'start': 10813.971, 'title': 'Initializing rnn and lstm layers', 'summary': 'Explores the process of initializing the rnn, adding lstm layers and dropout regularization to prevent overtraining, with a focus on the dimensionality of the output space and the random selection of neurons for training.', 'duration': 150.1, 'highlights': ['The dropout layer helps prevent overtraining by randomly turning off 20% of neurons during each training cycle.', 'The LSTM layer is initialized with 50 units, maintaining the return sequence true for sequence data, and the input shape is determined from the X train shape of 1 comma 1.', "The process involves adding three sets of LSTM layers followed by dropout layers to the sequential model to optimize the network's performance."]}, {'end': 11161.606, 'start': 10964.071, 'title': 'Building rnn model with keras', 'summary': 'Discusses the process of building a recurrent neural network (rnn) model with keras, explaining the automatic shape recognition, layer composition, and the compilation and training of the model with atom optimizer and mean squared value loss.', 'duration': 197.535, 'highlights': ['The model automatically recognizes the shape and sets the units, reducing the need for manual input, which contributes to the efficiency and ease of building RNN models.', 'Keras provides advanced options for layer interface and data input, leading to a more customized and impactful output, despite the additional steps involved in building the model.', 'The compilation and training of the RNN model involve using the Atom optimizer, optimized for big data, and mean squared value loss to assess the error, ensuring a comprehensive and efficient training process.']}, {'end': 11836.004, 'start': 11161.606, 'title': 'Neural networks tutorial summary', 'summary': "Outlines the process of running a neural network model to make predictions on stock prices with a focus on managing resources, data processing, training duration, testing, and visualization, culminating in evaluating the model's prediction accuracy.", 'duration': 674.398, 'highlights': ['The process of running a neural network model is outlined, emphasizing the importance of managing resources and efficiently processing large datasets to avoid memory overload and resource problems, with an example of potential memory usage reaching a gigabyte. Importance of managing resources and efficiently processing large datasets, potential memory usage reaching a gigabyte', 'The training duration of the neural network model is discussed, noting the time taken for training, with an older laptop running at 0.9 gigahertz on a dual processor, resulting in a runtime of approximately 20 to 30 minutes. Training duration discussion, runtime of approximately 20 to 30 minutes', "The processing and visualization of the test data to evaluate the model's predictions is detailed, including steps such as loading the test data, creating inputs, reshaping data, and making predictions, emphasizing the efficiency of neural networks in comparison to the training process. Processing and visualization of test data, efficiency of neural networks in comparison to the training process", "The visualization of the model's predictions against real stock data is demonstrated, highlighting the comparative accuracy of the predictions and the potential for improvement with deeper analysis and additional data, while also acknowledging the limitations of focusing solely on the opening price of the stock. Visualization of model's predictions, comparative accuracy, potential for improvement with deeper analysis, limitations of focusing solely on the opening price"]}], 'duration': 1022.033, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ob1yS9g-Zcs/pics/ob1yS9g-Zcs10813971.jpg', 'highlights': ["The process involves adding three sets of LSTM layers followed by dropout layers to the sequential model to optimize the network's performance.", 'The dropout layer helps prevent overtraining by randomly turning off 20% of neurons during each training cycle.', 'The LSTM layer is initialized with 50 units, maintaining the return sequence true for sequence data, and the input shape is determined from the X train shape of 1 comma 1.', 'The compilation and training of the RNN model involve using the Atom optimizer, optimized for big data, and mean squared value loss to assess the error, ensuring a comprehensive and efficient training process.', 'The model automatically recognizes the shape and sets the units, reducing the need for manual input, which contributes to the efficiency and ease of building RNN models.', "The processing and visualization of the test data to evaluate the model's predictions is detailed, including steps such as loading the test data, creating inputs, reshaping data, and making predictions, emphasizing the efficiency of neural networks in comparison to the training process.", "The visualization of the model's predictions against real stock data is demonstrated, highlighting the comparative accuracy of the predictions and the potential for improvement with deeper analysis and additional data, while also acknowledging the limitations of focusing solely on the opening price of the stock.", 'The training duration of the neural network model is discussed, noting the time taken for training, with an older laptop running at 0.9 gigahertz on a dual processor, resulting in a runtime of approximately 20 to 30 minutes.']}], 'highlights': ['Neural networks form the base of deep learning, inspired by the structure of the human brain.', 'Facial recognition cameras on smartphones showcase neural networks at play in differentiating faces and forecasting age.', 'Neural networks are expected to have applications in diverse areas such as medicine, agriculture, and physics.', 'Companies like Google, Amazon, and Nvidia have invested in developing products to support neural networks.', 'Artificial neural networks utilize input layers, hidden layers, and output layers for information processing.', 'Neural networks are trained to understand patterns and detect possibilities with high accuracy.', 'The future will offer more personalized choices for users and customers, including applications in online ordering systems and virtual assistants.', 'Neural networks learn by example, eliminating the need for in-depth programming.', 'Artificial neural networks have the potential for high fault tolerance.', 'Neural networks may take hours or even months to train, but time is a reasonable trade-off when compared to its scope.', 'The model predicts the outcome by applying an activation function to the output layer, and any error in the output is back-propagated through the network to adjust weights and minimize the error rate.', 'The input layer passes data to the hidden layer, where two hidden layers with interconnections are assigned weights at random.', 'The image is fed as a 28x28 pixel input to identify the registration plate using neural networks.', 'Keras is used for preprocessing images, including reshaping data, rescaling, and applying data augmentation techniques.', "Separate training and testing data sets are used to train and evaluate the neural network's performance.", 'The process involves creating the training set with 800 images using the image data generator and setting the target size to 64x64.', 'The test set is created with 2000 images using the image data generator and rescaling the images to 1 over 255.', 'Changed steps per epoch from 8,000 to 4,000 and epochs from 25 to 10, resulting in reduced processing time and increased efficiency.', 'Neural network achieved an accuracy of 86% on test data, effectively labeling dogs and cats based on images.', 'The potential applications of image classification in various industries are highlighted, showcasing its diverse uses.', 'The chapter introduces convolutional neural networks, emphasizing their role in image recognition and the layered process of feature extraction and pattern detection.', 'The neural network is trained to recognize handwritten alphabets A, B, and C using backpropagation and gradient descent.', 'The CNN structure and functioning for image recognition is explained, including the process of filtering, pooling, flattening, and classification.', 'The process of filtering, pooling, flattening, and classification in a Convolutional Neural Network (CNN) is explained, with a practical application using the CIFAR-10 dataset for classifying images across 10 categories.', 'Reshaping 10,000 images into a 10,000x3x32x32 array, representing 10,000 images with color channels and dimensions of 32x32.', 'The process of creating batches for handling large amounts of image data, emphasizing the importance of breaking up the data into smaller batch sizes, such as 100 images, and reshaping the data into the correct format for processing.', 'The model processes 50,000 pictures in batches of 100 during the training session.', 'The accuracy of the model improves to 0.39 after running the full training.', 'The chapter discusses the process of creating a TensorFlow model, including the use of placeholders for input data and dropout probability.', 'The definition of helper functions for weight and bias initialization, convolutional and pooling layers is explained in detail.', 'The LSTM layer is initialized with 50 units, maintaining the return sequence true for sequence data, and the input shape is determined from the X train shape of 1 comma 1.', 'The compilation and training of the RNN model involve using the Atom optimizer, optimized for big data, and mean squared value loss to assess the error, ensuring a comprehensive and efficient training process.']}