title

MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention

description

MIT Introduction to Deep Learning 6.S191: Lecture 2
Recurrent Neural Networks
Lecturer: Ava Amini
2023 Edition
For all lectures, slides, and lab materials: http://introtodeeplearning.com
Lecture Outline
0:00 - Introduction
3:07 - Sequence modeling
5:09 - Neurons with recurrence
12:05 - Recurrent neural networks
13:47 - RNN intuition
15:03 - Unfolding RNNs
18:57 - RNNs from scratch
21:50 - Design criteria for sequential modeling
23:45 - Word prediction example
29:57 - Backpropagation through time
32:25 - Gradient issues
37:03 - Long short term memory (LSTM)
39:50 - RNN applications
44:50 - Attention fundamentals
48:10 - Intuition of attention
50:30 - Attention and search relationship
52:40 - Learning attention with neural networks
58:16 - Scaling attention and applications
1:02:02 - Summary
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detail

{'title': 'MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention', 'heatmap': [{'end': 757.547, 'start': 716.794, 'weight': 0.786}, {'end': 947.538, 'start': 905.506, 'weight': 0.779}, {'end': 1173.599, 'start': 1125.231, 'weight': 0.758}, {'end': 1886.426, 'start': 1845.463, 'weight': 0.701}, {'end': 1962.23, 'start': 1923.082, 'weight': 0.757}, {'end': 2238.42, 'start': 2145.515, 'weight': 0.785}, {'end': 3318.797, 'start': 3125.151, 'weight': 0.784}], 'summary': 'Covers sequence modeling with neural networks, including the essentials of handling sequential data, applications in real-world scenarios, rnn computation, tensorflow implementation, training challenges, rnn limitations, self-attention, and neural network attention mechanism, providing comprehensive insights into the key aspects of sequence modeling and its applications.', 'chapters': [{'end': 122.386, 'segs': [{'end': 37.677, 'src': 'embed', 'start': 9.693, 'weight': 2, 'content': [{'end': 13.176, 'text': "Hello, everyone, and I hope you enjoyed Alexander's first lecture.", 'start': 9.693, 'duration': 3.483}, {'end': 24.405, 'text': "I'm Ava and in this second lecture lecture two we're going to focus on this question of sequence modeling how we can build neural networks that can handle and learn from sequential data.", 'start': 13.897, 'duration': 10.508}, {'end': 31.792, 'text': "So, in Alexander's first lecture, he introduced the essentials of neural networks, starting with perceptrons,", 'start': 25.486, 'duration': 6.306}, {'end': 37.677, 'text': 'building up to feed forward models and how you can actually train these models and start to think about deploying them forward.', 'start': 31.792, 'duration': 5.885}], 'summary': 'Ava introduces second lecture on sequence modeling in neural networks.', 'duration': 27.984, 'max_score': 9.693, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo9693.jpg'}, {'end': 106.176, 'src': 'embed', 'start': 55.563, 'weight': 0, 'content': [{'end': 60.844, 'text': 'And I think some of the components in this lecture traditionally can be a bit confusing or daunting at first.', 'start': 55.563, 'duration': 5.281}, {'end': 67.826, 'text': 'But what I really really want to do is to build this understanding up from the foundations, walking through step by step,', 'start': 61.244, 'duration': 6.582}, {'end': 74.527, 'text': 'developing intuition all the way to understanding the math and the operations behind how these networks operate.', 'start': 67.826, 'duration': 6.701}, {'end': 77.868, 'text': "Okay, so let's get started.", 'start': 75.567, 'duration': 2.301}, {'end': 88.752, 'text': 'To begin, I first want to motivate what exactly we mean when we talk about sequential data or sequential modeling.', 'start': 80.202, 'duration': 8.55}, {'end': 91.995, 'text': "So we're gonna begin with a really simple, intuitive example.", 'start': 89.212, 'duration': 2.783}, {'end': 99.464, 'text': "Let's say we have this picture of a ball, and your task is to predict where this ball is going to travel to next.", 'start': 92.816, 'duration': 6.648}, {'end': 106.176, 'text': "Now, if you don't have any prior information about the trajectory of the ball, its motion, its history,", 'start': 100.509, 'duration': 5.667}], 'summary': 'The lecture aims to demystify sequential modeling by starting from foundational concepts and developing intuition through step-by-step explanations.', 'duration': 50.613, 'max_score': 55.563, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo55563.jpg'}], 'start': 9.693, 'title': 'Sequence modeling with neural networks', 'summary': 'Delves into the essentials of sequence modeling using neural networks, highlighting the need for a distinct approach in handling sequential data and the importance of prior information in predictions.', 'chapters': [{'end': 122.386, 'start': 9.693, 'title': 'Sequence modeling with neural networks', 'summary': 'Focuses on the essentials of sequence modeling using neural networks, emphasizing the need for a different approach in handling sequential data and the significance of prior information in making predictions.', 'duration': 112.693, 'highlights': ['The significance of prior information in making predictions is emphasized using the example of predicting the trajectory of a ball, where the addition of historical motion information makes the prediction task easier.', 'The lecture aims to build understanding from foundational concepts to the mathematical operations behind neural networks, with a focus on developing intuition step by step.', 'The second lecture delves into the specifics of building neural networks for sequential data processing, following the introduction of neural network essentials in the first lecture.']}], 'duration': 112.693, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo9693.jpg', 'highlights': ['The significance of prior information in making predictions is emphasized using the example of predicting the trajectory of a ball, where the addition of historical motion information makes the prediction task easier.', 'The lecture aims to build understanding from foundational concepts to the mathematical operations behind neural networks, with a focus on developing intuition step by step.', 'The second lecture delves into the specifics of building neural networks for sequential data processing, following the introduction of neural network essentials in the first lecture.']}, {'end': 804.567, 'segs': [{'end': 166.832, 'src': 'embed', 'start': 122.486, 'weight': 0, 'content': [{'end': 132.217, 'text': 'And I think hopefully we can all agree that our most likely next prediction is that this ball is going to move forward to the right in the next frame.', 'start': 122.486, 'duration': 9.731}, {'end': 137.579, 'text': 'So this is a really reduced down, bare bones, intuitive example.', 'start': 133.598, 'duration': 3.981}, {'end': 143.141, 'text': 'But the truth is that beyond this, sequential data is really all around us.', 'start': 138.2, 'duration': 4.941}, {'end': 144.662, 'text': "As I'm speaking,", 'start': 143.421, 'duration': 1.241}, {'end': 152.424, 'text': 'the words coming out of my mouth form a sequence of sound waves that define audio which we can split up to think about in this sequential manner.', 'start': 144.662, 'duration': 7.762}, {'end': 160.227, 'text': 'Similarly, text, language can be split up into a sequence of characters or a sequence of words.', 'start': 153.264, 'duration': 6.963}, {'end': 166.832, 'text': 'And there are many, many, many more examples in which sequential processing, sequential data is present right?', 'start': 161.428, 'duration': 5.404}], 'summary': 'Sequential data is all around us, from sound waves to text and language.', 'duration': 44.346, 'max_score': 122.486, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo122486.jpg'}, {'end': 230.703, 'src': 'embed', 'start': 190.325, 'weight': 3, 'content': [{'end': 193.969, 'text': 'When we consider applications of sequential modeling in the real world,', 'start': 190.325, 'duration': 3.644}, {'end': 199.396, 'text': 'we can think about a number of different kind of problem definitions that we can have in our arsenal and work with.', 'start': 193.969, 'duration': 5.427}, {'end': 206.063, 'text': 'In the first lecture, Alexander introduced the notions of classification and the notion of regression.', 'start': 200.277, 'duration': 5.786}, {'end': 213.675, 'text': 'where he talked about, and we learned about feed-forward models that can operate one-to-one in this fixed and static setting right?', 'start': 206.789, 'duration': 6.886}, {'end': 216.638, 'text': 'Given a single input, predict a single output.', 'start': 214.036, 'duration': 2.602}, {'end': 224.546, 'text': "The binary classification example of will you succeed or pass this class? And here, there's no notion of sequence.", 'start': 217.419, 'duration': 7.127}, {'end': 225.687, 'text': "There's no notion of time.", 'start': 224.586, 'duration': 1.101}, {'end': 230.703, 'text': 'Now, if we introduce this idea of a sequential component,', 'start': 227.222, 'duration': 3.481}], 'summary': 'Sequential modeling can be applied to classification and regression problems, with feed-forward models operating in a fixed, static setting.', 'duration': 40.378, 'max_score': 190.325, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo190325.jpg'}, {'end': 326.958, 'src': 'embed', 'start': 302.282, 'weight': 4, 'content': [{'end': 309.592, 'text': "And from this, we're going to start and build up our understanding of what neural networks we can build and train for these types of problems.", 'start': 302.282, 'duration': 7.31}, {'end': 319.073, 'text': "So first, we're going to begin with the notion of recurrence and build up from that to define recurrent neural networks.", 'start': 311.468, 'duration': 7.605}, {'end': 326.958, 'text': "And in the last portion of the lecture, we'll talk about the underlying mechanisms underlying the transformer architectures that are very, very,", 'start': 319.493, 'duration': 7.465}], 'summary': 'Lecture covers building neural networks for recurrent and transformer architectures.', 'duration': 24.676, 'max_score': 302.282, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo302282.jpg'}, {'end': 757.547, 'src': 'heatmap', 'start': 716.794, 'weight': 0.786, 'content': [{'end': 724.478, 'text': 'And the key here is that we have this idea of this recurrence relation that captures the cyclic temporal dependency.', 'start': 716.794, 'duration': 7.684}, {'end': 732.671, 'text': "And indeed, it's this idea that is really the intuitive foundation behind recurrent neural networks or RNNs.", 'start': 726.348, 'duration': 6.323}, {'end': 742.777, 'text': "And so let's continue to build up our understanding from here and move forward into how we can actually define the RNN operations mathematically and in code.", 'start': 733.492, 'duration': 9.285}, {'end': 746.859, 'text': "So all we're gonna do is formalize this relationship a little bit more.", 'start': 743.837, 'duration': 3.022}, {'end': 757.547, 'text': "The key idea here is that the RNN is maintaining the state and it's updating the state at each of these time steps as the sequence is processed.", 'start': 748.015, 'duration': 9.532}], 'summary': 'Rnn captures cyclic temporal dependency in maintaining and updating state at each time step.', 'duration': 40.753, 'max_score': 716.794, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo716794.jpg'}], 'start': 122.486, 'title': 'Sequential data processing and modeling', 'summary': 'Discusses the commonality of sequential data in sound waves, text, and language, and explores the applications of sequential modeling in real-world scenarios, including classification, regression, limitations of feed-forward models, and the foundations of recurrent neural networks.', 'chapters': [{'end': 166.832, 'start': 122.486, 'title': 'Sequential data processing', 'summary': 'Discusses the presence of sequential data in various forms such as sound waves, text, and language, emphasizing the commonality of sequential processing in our surroundings.', 'duration': 44.346, 'highlights': ['Sequential data is prevalent in various forms like sound waves, text, and language, reflecting the ubiquity of sequential processing in our environment.', 'The prediction that the ball will move forward to the right in the next frame serves as a simplified illustration of sequential data processing.', 'Sequential data is omnipresent, extending beyond simple examples and encompassing diverse instances in our surroundings.']}, {'end': 804.567, 'start': 167.353, 'title': 'Sequential modeling and neural networks', 'summary': 'Explores the applications of sequential modeling in real-world scenarios, introduces the concepts of classification and regression, explains the limitations of feed-forward models, and delves into the foundations of recurrent neural networks for handling sequential data.', 'duration': 637.214, 'highlights': ['The chapter explores the applications of sequential modeling in real-world scenarios.', 'Introduces the concepts of classification and regression in the context of sequential modeling.', 'Explains the limitations of feed-forward models in handling temporal and sequential data.', 'Delves into the foundations of recurrent neural networks for handling sequential data.']}], 'duration': 682.081, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo122486.jpg', 'highlights': ['Sequential data is prevalent in various forms like sound waves, text, and language, reflecting the ubiquity of sequential processing in our environment.', 'The prediction that the ball will move forward to the right in the next frame serves as a simplified illustration of sequential data processing.', 'Sequential data is omnipresent, extending beyond simple examples and encompassing diverse instances in our surroundings.', 'The chapter explores the applications of sequential modeling in real-world scenarios.', 'Delves into the foundations of recurrent neural networks for handling sequential data.', 'Introduces the concepts of classification and regression in the context of sequential modeling.', 'Explains the limitations of feed-forward models in handling temporal and sequential data.']}, {'end': 1204.075, 'segs': [{'end': 904.525, 'src': 'embed', 'start': 880.392, 'weight': 1, 'content': [{'end': 886.273, 'text': 'And this is what generates a prediction for the next word and updates the RNN state in turn.', 'start': 880.392, 'duration': 5.881}, {'end': 890.757, 'text': 'Finally, our prediction for the final word in the sentence.', 'start': 887.435, 'duration': 3.322}, {'end': 894.039, 'text': "the word that we're missing is simply the RNN's output.", 'start': 890.757, 'duration': 3.282}, {'end': 897.241, 'text': 'after all, the prior words have been fed in through the model.', 'start': 894.039, 'duration': 3.202}, {'end': 904.525, 'text': "So this is really breaking down how the RNN works, how it's processing the sequential information.", 'start': 899.242, 'duration': 5.283}], 'summary': "Explains rnn's prediction for next word, updating state, and processing sequential information.", 'duration': 24.133, 'max_score': 880.392, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo880392.jpg'}, {'end': 947.538, 'src': 'heatmap', 'start': 905.506, 'weight': 0.779, 'content': [{'end': 914.151, 'text': "And what you've noticed is that the RNN computation includes both this update to the hidden state as well as generating some predicted output at the end.", 'start': 905.506, 'duration': 8.645}, {'end': 916.633, 'text': "That is our ultimate goal that we're interested in.", 'start': 914.491, 'duration': 2.142}, {'end': 927.943, 'text': "And so to walk through this, how we're actually generating the output prediction itself, what the RNN computes is given some input vector.", 'start': 917.731, 'duration': 10.212}, {'end': 930.226, 'text': 'it then performs this update to the hidden state.', 'start': 927.943, 'duration': 2.283}, {'end': 938.85, 'text': 'And this update to the hidden state is just a standard neural network operation, just like we saw in the first lecture,', 'start': 931.923, 'duration': 6.927}, {'end': 947.538, 'text': 'where it consists of taking a weight matrix, multiplying that by the previous hidden state, taking another weight matrix,', 'start': 938.85, 'duration': 8.688}], 'summary': 'Rnn computes hidden state update and output prediction.', 'duration': 42.032, 'max_score': 905.506, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo905506.jpg'}, {'end': 1025.017, 'src': 'embed', 'start': 977.93, 'weight': 0, 'content': [{'end': 982.997, 'text': 'using a separate weight matrix to update this value and then generate a predicted output.', 'start': 977.93, 'duration': 5.067}, {'end': 986.422, 'text': "And that's what there is to it right?", 'start': 984.459, 'duration': 1.963}, {'end': 994.714, 'text': "That's how the RNN, in its single operation, updates both the hidden state and also generates a predicted output.", 'start': 986.863, 'duration': 7.851}, {'end': 1003.97, 'text': 'Okay, so now this gives you the internal working of how the RNN computation occurs at a particular time step.', 'start': 996.348, 'duration': 7.622}, {'end': 1015.694, 'text': "Let's next think about how this looks like over time and define the computational graph of the RNN as being unrolled or expanded across time.", 'start': 1004.671, 'duration': 11.023}, {'end': 1025.017, 'text': "So, so far, the dominant way I've been showing the RNNs is according to this loop-like diagram on the left, right, feeding back in on itself.", 'start': 1016.694, 'duration': 8.323}], 'summary': 'Rnn updates hidden state and generates output in single operation.', 'duration': 47.087, 'max_score': 977.93, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo977930.jpg'}, {'end': 1179.703, 'src': 'heatmap', 'start': 1125.231, 'weight': 3, 'content': [{'end': 1132.494, 'text': 'And finally, we can get our total loss by taking all these individual loss terms together and summing them,', 'start': 1125.231, 'duration': 7.263}, {'end': 1136.136, 'text': 'defining the total loss for a particular input to the RNN.', 'start': 1132.494, 'duration': 3.642}, {'end': 1143.41, 'text': 'If we can walk through an example of how we implement this RNN in TensorFlow starting from scratch.', 'start': 1138.226, 'duration': 5.184}, {'end': 1151.515, 'text': 'The RNN can be defined as a layer operation and layer class that Alexander introduced in the first lecture.', 'start': 1144.53, 'duration': 6.985}, {'end': 1159.921, 'text': 'And so we can define it according to an initialization of weight matrices, initialization of a hidden state,', 'start': 1152.256, 'duration': 7.665}, {'end': 1163.804, 'text': 'which commonly amounts to initializing these two to zero.', 'start': 1159.921, 'duration': 3.883}, {'end': 1173.599, 'text': 'Next, we can define how we can actually pass forward through the RNN network to process a given input X.', 'start': 1165.532, 'duration': 8.067}, {'end': 1179.703, 'text': "And what you'll notice is in this forward operation, the computations are exactly like we just walked through.", 'start': 1173.599, 'duration': 6.104}], 'summary': 'The total loss for a particular input to the rnn is computed by summing individual loss terms. the rnn implementation in tensorflow involves defining an rnn as a layer operation and class, initializing weight matrices and hidden state, and passing forward through the rnn network to process a given input x.', 'duration': 54.472, 'max_score': 1125.231, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo1125231.jpg'}], 'start': 804.567, 'title': 'Understanding rnn computation', 'summary': 'Delves into the processing of sequential data by recurrent neural networks, covering key aspects such as the update operation, implementation in python, internal working, unrolling across time, weight matrices, training, and implementation in tensorflow.', 'chapters': [{'end': 1204.075, 'start': 804.567, 'title': 'Understanding rnn computation', 'summary': 'Explains how recurrent neural networks process sequential data, including the update operation, implementation in python, internal working, unrolling across time, weight matrices, training, and implementation in tensorflow.', 'duration': 399.508, 'highlights': ['Recurrent neural networks process sequential data by updating the hidden state and generating predictions for the next word at each time step, ultimately resulting in the final word prediction.', 'The RNN computation involves updating the hidden state by multiplying weight matrices with the previous hidden state and input at a time step, applying a non-linearity, and generating a predicted output through a separate weight matrix.', "The RNN's computational graph can be unrolled across time, defining weight matrices for connecting inputs to the hidden state update, internal state across time, and generating predicted output, while reusing the same weight matrices for all time steps.", 'Training the RNN involves computing loss at each time step by comparing predictions to true labels, and obtaining the total loss by summing individual loss terms for a particular input to the RNN.', 'Implementing the RNN in TensorFlow entails defining weight matrices, initializing a hidden state, and passing forward through the RNN network to process a given input X, updating the hidden state and generating a predicted output at each time step.']}], 'duration': 399.508, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo804567.jpg', 'highlights': ['The RNN computation involves updating the hidden state by multiplying weight matrices with the previous hidden state and input at a time step, applying a non-linearity, and generating a predicted output through a separate weight matrix.', 'Recurrent neural networks process sequential data by updating the hidden state and generating predictions for the next word at each time step, ultimately resulting in the final word prediction.', "The RNN's computational graph can be unrolled across time, defining weight matrices for connecting inputs to the hidden state update, internal state across time, and generating predicted output, while reusing the same weight matrices for all time steps.", 'Training the RNN involves computing loss at each time step by comparing predictions to true labels, and obtaining the total loss by summing individual loss terms for a particular input to the RNN.', 'Implementing the RNN in TensorFlow entails defining weight matrices, initializing a hidden state, and passing forward through the RNN network to process a given input X, updating the hidden state and generating a predicted output at each time step.']}, {'end': 1753.727, 'segs': [{'end': 1254.725, 'src': 'embed', 'start': 1205.56, 'weight': 0, 'content': [{'end': 1212.185, 'text': 'What is very convenient is that, although you could define your RNN network and your RNN layer completely from scratch,', 'start': 1205.56, 'duration': 6.625}, {'end': 1215.447, 'text': 'is that TensorFlow abstracts this operation away for you.', 'start': 1212.185, 'duration': 3.262}, {'end': 1228.156, 'text': "So you can simply define a simple RNN according to this call that you're seeing here, which makes all the computations very efficient and very easy.", 'start': 1215.947, 'duration': 12.209}, {'end': 1234.34, 'text': "And you'll actually get practice implementing and working with RNNs in today's software lab.", 'start': 1229.016, 'duration': 5.324}, {'end': 1240.194, 'text': 'Okay, so that gives us the understanding of RNNs.', 'start': 1236.351, 'duration': 3.843}, {'end': 1248.54, 'text': 'and going back to what I described as kind of the problem setups or the problem definitions at the beginning of this lecture,', 'start': 1240.194, 'duration': 8.346}, {'end': 1254.725, 'text': 'I just want to remind you of the types of sequence modeling problems on which we can apply RNNs right?', 'start': 1248.54, 'duration': 6.185}], 'summary': "Tensorflow abstracts rnn network definition for efficiency. practice rnn implementation in today's lab.", 'duration': 49.165, 'max_score': 1205.56, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo1205560.jpg'}, {'end': 1303.051, 'src': 'embed', 'start': 1276.824, 'weight': 4, 'content': [{'end': 1283.03, 'text': 'step in that sequence and then doing this sequence to sequence, type of prediction and translation.', 'start': 1276.824, 'duration': 6.206}, {'end': 1294.507, 'text': 'Okay, So Yeah, so this will be the foundation for the software lab today,', 'start': 1283.05, 'duration': 11.457}, {'end': 1303.051, 'text': 'which will focus on this problem of many to many processing and many to many sequential modeling taking a sequence, going to a sequence.', 'start': 1294.507, 'duration': 8.544}], 'summary': "Today's software lab focuses on many-to-many processing and sequential modeling.", 'duration': 26.227, 'max_score': 1276.824, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo1276824.jpg'}, {'end': 1401.889, 'src': 'embed', 'start': 1373.686, 'weight': 2, 'content': [{'end': 1376.228, 'text': 'Next, sequence is all about order.', 'start': 1373.686, 'duration': 2.542}, {'end': 1382.053, 'text': "There's some notion of how current inputs depend on prior inputs,", 'start': 1376.588, 'duration': 5.465}, {'end': 1390.119, 'text': 'and the specific order of observations we see makes a big effect on what prediction we may want to generate at the end.', 'start': 1382.053, 'duration': 8.066}, {'end': 1401.889, 'text': 'And finally, in order to be able to process this information effectively, our network needs to be able to do what we call parameter sharing,', 'start': 1391.823, 'duration': 10.066}], 'summary': 'Sequence implies order, impacting predictions. parameter sharing is crucial for effective information processing.', 'duration': 28.203, 'max_score': 1373.686, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo1373686.jpg'}, {'end': 1529.334, 'src': 'embed', 'start': 1504.103, 'weight': 5, 'content': [{'end': 1509.566, 'text': "It doesn't have an understanding from the start of what a word is or what language means,", 'start': 1504.103, 'duration': 5.463}, {'end': 1517.149, 'text': 'which means that we need a way to represent language numerically so that it can be passed in to the network to process.', 'start': 1509.566, 'duration': 7.583}, {'end': 1529.334, 'text': 'So what we do is that we need to define a way to translate this text, this language information, into a numerical, encoding a vector,', 'start': 1519.286, 'duration': 10.048}], 'summary': 'Language must be numerically represented for processing, requiring translation into a vector.', 'duration': 25.231, 'max_score': 1504.103, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo1504103.jpg'}, {'end': 1736.083, 'src': 'embed', 'start': 1709.286, 'weight': 1, 'content': [{'end': 1716.028, 'text': 'If we again want to predict the next word in the sequence, we can have short sequences. we can have long sequences. we can have even longer sentences.', 'start': 1709.286, 'duration': 6.742}, {'end': 1722.39, 'text': 'And our key task is that we want to be able to track dependencies across all these different lengths.', 'start': 1716.748, 'duration': 5.642}, {'end': 1729.633, 'text': 'And what we mean by dependencies is that there could be information very, very early on in a sequence,', 'start': 1723.471, 'duration': 6.162}, {'end': 1736.083, 'text': 'but that may not be relevant or come up late until very much later in the sequence.', 'start': 1730.495, 'duration': 5.588}], 'summary': 'Predict next word with short, long, and even longer sequences while tracking dependencies across different lengths.', 'duration': 26.797, 'max_score': 1709.286, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo1709286.jpg'}], 'start': 1205.56, 'title': 'Rnn in tensorflow', 'summary': 'Introduces recurrent neural networks (rnns) and their applications in sequence modeling, including single output prediction, text generation, and sequence-to-sequence prediction in tensorflow. it also discusses the design criteria for building a robust and reliable rnn for processing sequential modeling problems, emphasizing the need to handle variable-length sequences, track dependencies across different lengths, maintain order, and implement parameter sharing.', 'chapters': [{'end': 1303.051, 'start': 1205.56, 'title': 'Introduction to rnn in tensorflow', 'summary': 'Introduces recurrent neural networks (rnns) and their applications in sequence modeling, including single output prediction, text generation, and sequence-to-sequence prediction, and emphasizes the convenience of defining rnn networks in tensorflow.', 'duration': 97.491, 'highlights': ['The chapter emphasizes the convenience of defining RNN networks in TensorFlow, abstracting the operation away for efficient and easy computations.', 'The chapter introduces the applications of RNNs in sequence modeling, including single output prediction, text generation, and sequence-to-sequence prediction.', 'The chapter mentions that the software lab will focus on the problem of many-to-many processing and many-to-many sequential modeling.']}, {'end': 1753.727, 'start': 1304.778, 'title': 'Rnn design criteria', 'summary': 'Discusses the design criteria for building a robust and reliable recurrent neural network (rnn) for processing sequential modeling problems, emphasizing the need to handle variable-length sequences, track dependencies across different lengths, maintain order, and implement parameter sharing, while also addressing the challenge of representing language numerically for the network to process effectively.', 'duration': 448.949, 'highlights': ['The need to handle variable-length sequences and track dependencies across different lengths is crucial for processing sequential data effectively.', 'Maintaining order and implementing parameter sharing are essential for processing sequential data effectively.', 'Representing language numerically for the network to process effectively creates the challenge of transforming text-based data into a numerical encoding, often addressed through the concept of embedding.']}], 'duration': 548.167, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo1205560.jpg', 'highlights': ['The chapter introduces the applications of RNNs in sequence modeling, including single output prediction, text generation, and sequence-to-sequence prediction.', 'The need to handle variable-length sequences and track dependencies across different lengths is crucial for processing sequential data effectively.', 'Maintaining order and implementing parameter sharing are essential for processing sequential data effectively.', 'The chapter emphasizes the convenience of defining RNN networks in TensorFlow, abstracting the operation away for efficient and easy computations.', 'The chapter mentions that the software lab will focus on the problem of many-to-many processing and many-to-many sequential modeling.', 'Representing language numerically for the network to process effectively creates the challenge of transforming text-based data into a numerical encoding, often addressed through the concept of embedding.']}, {'end': 2513.481, 'segs': [{'end': 1827.127, 'src': 'embed', 'start': 1802.919, 'weight': 2, 'content': [{'end': 1809.063, 'text': "And that's done through backpropagation algorithm with a bit of a twist to just handle sequential information.", 'start': 1802.919, 'duration': 6.144}, {'end': 1821.199, 'text': 'If we go back and think about how we train feedforward neural network models, the steps break down in thinking through, starting with an input,', 'start': 1811.144, 'duration': 10.055}, {'end': 1827.127, 'text': 'where we first take this input and make a forward pass through the network, going from input to output.', 'start': 1821.199, 'duration': 5.928}], 'summary': 'Training neural network models using backpropagation algorithm for sequential information.', 'duration': 24.208, 'max_score': 1802.919, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo1802919.jpg'}, {'end': 1903.136, 'src': 'heatmap', 'start': 1845.463, 'weight': 1, 'content': [{'end': 1851.649, 'text': 'in order to gradually adjust the parameters, the weights of the network, in order to minimize the overall loss.', 'start': 1845.463, 'duration': 6.186}, {'end': 1856.082, 'text': 'Now with RNNs, as we walked through earlier.', 'start': 1853.322, 'duration': 2.76}, {'end': 1858.303, 'text': 'we have this temporal unrolling,', 'start': 1856.082, 'duration': 2.221}, {'end': 1866.164, 'text': 'which means that we have these individual losses across the individual steps in our sequence that sum together to comprise the overall loss.', 'start': 1858.303, 'duration': 7.861}, {'end': 1877.506, 'text': 'What this means is that when we do backpropagation, we have to now, instead of backpropagating errors through a single network,', 'start': 1867.845, 'duration': 9.661}, {'end': 1880.927, 'text': 'backpropagate the loss through each of these individual time steps.', 'start': 1877.506, 'duration': 3.421}, {'end': 1886.426, 'text': 'And after we back-propagate loss through each of the individual time steps,', 'start': 1881.983, 'duration': 4.443}, {'end': 1894.611, 'text': 'we then do that across all time steps all the way from our current time time t back to the beginning of the sequence.', 'start': 1886.426, 'duration': 8.185}, {'end': 1900.554, 'text': 'And this is why this algorithm is called backpropagation through time.', 'start': 1895.851, 'duration': 4.703}, {'end': 1903.136, 'text': 'Because, as you can see,', 'start': 1901.675, 'duration': 1.461}], 'summary': 'Rnns use backpropagation through time to adjust network weights and minimize overall loss through individual time steps.', 'duration': 25.63, 'max_score': 1845.463, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo1845463.jpg'}, {'end': 1977.615, 'src': 'heatmap', 'start': 1923.082, 'weight': 7, 'content': [{'end': 1931.345, 'text': 'And the reason for this is, if we take a close look looking at how gradients flow across the RNN, what this algorithm involves is many,', 'start': 1923.082, 'duration': 8.263}, {'end': 1937.746, 'text': 'many repeated computations and multiplications of these weight matrices repeatedly against each other.', 'start': 1931.345, 'duration': 6.401}, {'end': 1948.329, 'text': 'In order to compute the gradient with respect to the very first time step, we have to make many of these multiplicative repeats of the weight matrix.', 'start': 1938.786, 'duration': 9.543}, {'end': 1951.607, 'text': 'Why might this be problematic?', 'start': 1949.867, 'duration': 1.74}, {'end': 1962.23, 'text': 'Well, if this weight matrix W is very, very big, what this can result in is what we call the exploding gradient problem,', 'start': 1952.428, 'duration': 9.802}, {'end': 1966.611, 'text': "where our gradients that we're trying to use to optimize our network do exactly that.", 'start': 1962.23, 'duration': 4.381}, {'end': 1968.351, 'text': 'They blow up, they explode.', 'start': 1967.011, 'duration': 1.34}, {'end': 1975.113, 'text': 'And they get really big and makes it infeasible and not possible to train the network stably.', 'start': 1969.252, 'duration': 5.861}, {'end': 1977.615, 'text': 'What we do to mitigate.', 'start': 1976.414, 'duration': 1.201}], 'summary': 'Repeated multiplications of big weight matrices can lead to exploding gradients, making it infeasible to train the network stably.', 'duration': 27.748, 'max_score': 1923.082, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo1923082.jpg'}, {'end': 2238.42, 'src': 'heatmap', 'start': 2145.515, 'weight': 0.785, 'content': [{'end': 2153.721, 'text': 'So the ways and modifications that we can make to our network to try to alleviate this problem, threefold.', 'start': 2145.515, 'duration': 8.206}, {'end': 2173.327, 'text': 'The first is that we can simply change the activation functions in each of our neural network layers to be such that they can effectively try to mitigate and safeguard from gradients in instances where from shrinking the gradients in instances where the data is greater than zero.', 'start': 2154.781, 'duration': 18.546}, {'end': 2177.93, 'text': 'And this is in particular true for the ReLU activation function.', 'start': 2173.988, 'duration': 3.942}, {'end': 2186.617, 'text': 'And the reason is that in all instances where x is greater than zero, with the ReLU function, the derivative is one.', 'start': 2178.611, 'duration': 8.006}, {'end': 2195.283, 'text': 'And so that is not less than one, and therefore it helps in mitigating the vanishing gradient problem.', 'start': 2187.177, 'duration': 8.106}, {'end': 2205.181, 'text': 'Another trick is how we initialize the parameters in the network themselves to prevent them from shrinking to zero too rapidly.', 'start': 2197.273, 'duration': 7.908}, {'end': 2213.649, 'text': 'And there are mathematical ways that we can do this, namely by initializing our weights to identity matrices.', 'start': 2206.182, 'duration': 7.467}, {'end': 2221.037, 'text': 'And this effectively helps in practice to prevent the weight updates to shrink too rapidly to zero.', 'start': 2214.45, 'duration': 6.587}, {'end': 2238.42, 'text': 'However, the most robust solution to the vanishing gradient problem is by introducing a slightly more complicated version of the recurrent neural unit to be able to more effectively track and handle long-term dependencies in the data.', 'start': 2222.568, 'duration': 15.852}], 'summary': 'Three ways to mitigate vanishing gradient problem: change activation functions, initialize parameters, use a more robust version of recurrent neural unit.', 'duration': 92.905, 'max_score': 2145.515, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo2145515.jpg'}, {'end': 2221.037, 'src': 'embed', 'start': 2173.988, 'weight': 5, 'content': [{'end': 2177.93, 'text': 'And this is in particular true for the ReLU activation function.', 'start': 2173.988, 'duration': 3.942}, {'end': 2186.617, 'text': 'And the reason is that in all instances where x is greater than zero, with the ReLU function, the derivative is one.', 'start': 2178.611, 'duration': 8.006}, {'end': 2195.283, 'text': 'And so that is not less than one, and therefore it helps in mitigating the vanishing gradient problem.', 'start': 2187.177, 'duration': 8.106}, {'end': 2205.181, 'text': 'Another trick is how we initialize the parameters in the network themselves to prevent them from shrinking to zero too rapidly.', 'start': 2197.273, 'duration': 7.908}, {'end': 2213.649, 'text': 'And there are mathematical ways that we can do this, namely by initializing our weights to identity matrices.', 'start': 2206.182, 'duration': 7.467}, {'end': 2221.037, 'text': 'And this effectively helps in practice to prevent the weight updates to shrink too rapidly to zero.', 'start': 2214.45, 'duration': 6.587}], 'summary': 'Relu activation function helps mitigate vanishing gradient problem by maintaining derivatives greater than one, and weight initialization with identity matrices prevents rapid shrinkage of weight updates.', 'duration': 47.049, 'max_score': 2173.988, 'thumbnail': ''}, {'end': 2305.548, 'src': 'embed', 'start': 2274.393, 'weight': 0, 'content': [{'end': 2282.92, 'text': 'but I just want to convey the key idea and intuitive idea about why these LSTMs are effective at tracking long-term dependencies.', 'start': 2274.393, 'duration': 8.527}, {'end': 2297.485, 'text': 'The core is that the LSTM is able to control the flow of information through these gates to be able to more effectively filter out the unimportant things and store the important things.', 'start': 2284.141, 'duration': 13.344}, {'end': 2305.548, 'text': 'What you can do is implement LSTMs in TensorFlow just as you would an RNN.', 'start': 2299.686, 'duration': 5.862}], 'summary': 'Lstms are effective at tracking long-term dependencies by controlling information flow through gates.', 'duration': 31.155, 'max_score': 2274.393, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo2274393.jpg'}, {'end': 2473.577, 'src': 'embed', 'start': 2418.069, 'weight': 3, 'content': [{'end': 2430.034, 'text': "where you're going to work to build an RNN that can predict the next musical note in a sequence and use it to generate brand new musical sequences that have never been realized before.", 'start': 2418.069, 'duration': 11.965}, {'end': 2438.357, 'text': 'So to give you an example of just the quality and type of output that you can try to aim towards.', 'start': 2431.234, 'duration': 7.123}, {'end': 2445.791, 'text': 'a few years ago there was a work that trained in RNN on a corpus of classical music data.', 'start': 2438.357, 'duration': 7.434}, {'end': 2454.64, 'text': "And famously, there's this composer, Schubert, who wrote a famous unfinished symphony that consisted of two movements,", 'start': 2446.512, 'duration': 8.128}, {'end': 2459.305, 'text': 'but he was unable to finish his symphony before he died.', 'start': 2454.64, 'duration': 4.665}, {'end': 2463.168, 'text': 'So he died and then he left the third movement unfinished.', 'start': 2459.625, 'duration': 3.543}, {'end': 2473.577, 'text': "So, a few years ago a group trained a RNN-based model to actually try to generate the third movement to Schubert's famous Unfinished Symphony,", 'start': 2463.869, 'duration': 9.708}], 'summary': "Build rnn to predict musical notes and generate new sequences, e.g. completing schubert's unfinished symphony.", 'duration': 55.508, 'max_score': 2418.069, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo2418069.jpg'}], 'start': 1754.367, 'title': 'Rnn training and issues', 'summary': "Discusses rnn training and backpropagation, addressing challenges in handling sequential data, backpropagation through time algorithm, issues of exploding and vanishing gradients, and the effectiveness of lstms in tracking long-term dependencies. it also highlights applications in music generation, including a case of training an rnn to generate the third movement to schubert's unfinished symphony.", 'chapters': [{'end': 1922.322, 'start': 1754.367, 'title': 'Rnn training and backpropagation', 'summary': 'Discusses the process of training recurrent neural networks (rnns), emphasizing the challenges in handling sequential data and the backpropagation through time algorithm, which involves backpropagating gradients through individual time steps to minimize overall loss.', 'duration': 167.955, 'highlights': ['The backpropagation through time algorithm involves backpropagating errors through individual time steps, from the current time t back to the beginning of the sequence, to gradually adjust the weights of the network to minimize the overall loss.', 'RNNs require handling temporal unrolling, which results in individual losses across the steps in a sequence that sum together to form the overall loss, posing the challenge of backpropagating loss through each of these individual time steps.', 'Training RNNs involves using the backpropagation algorithm with a twist to handle sequential information, which entails defining and updating the loss with respect to each network parameter to gradually adjust the weights and minimize the overall loss.']}, {'end': 2221.037, 'start': 1923.082, 'title': 'Rnn gradient issues & solutions', 'summary': "Discusses the issues of exploding and vanishing gradients in rnns, with the former causing instability in training due to excessively large gradients and the latter hindering the network's ability to establish long-term dependencies, and provides solutions including gradient clipping, activation function adjustments, and parameter initialization.", 'duration': 297.955, 'highlights': ['The exploding gradient problem occurs when weight matrices are very large, leading to unstable network training due to excessively large gradients, which can be mitigated using gradient clipping to scale back the gradients.', 'The vanishing gradient problem arises when weight matrices are very small, resulting in the inability of the network to establish long-term dependencies due to gradients shrinking close to zero, which can be addressed through changes in activation functions and parameter initialization to prevent rapid shrinking of weights.', 'Changing the activation functions, particularly using ReLU, can effectively mitigate the vanishing gradient problem by ensuring that the derivative is not less than one in instances where x is greater than zero, thus preventing gradient shrinking.', 'Adjusting the parameter initialization by initializing weights to identity matrices can help prevent the weight updates from shrinking too rapidly to zero, thus addressing the vanishing gradient problem.']}, {'end': 2513.481, 'start': 2222.568, 'title': 'Lstm for long-term dependencies', 'summary': "Discusses the effectiveness of lstms in tracking long-term dependencies by controlling the flow of information through gates and highlights the application of rnn in music generation, including a case where an rnn was trained to generate the third movement to schubert's famous unfinished symphony.", 'duration': 290.913, 'highlights': ['The LSTM is effective at tracking long-term dependencies by controlling the flow of information through gates, filtering out unimportant things and storing the important ones, largely eliminating the vanishing gradient problem (quantifiable data: vanishing gradient problem)', "An example of RNN application is in music generation, where an RNN can predict the next musical note in a sequence and generate brand new musical sequences, such as the attempt to generate the third movement to Schubert's famous Unfinished Symphony (quantifiable data: third movement to Schubert's Unfinished Symphony)", "The RNN was trained on a corpus of classical music data to generate the third movement to Schubert's famous Unfinished Symphony, showcasing the quality of the music generated (quantifiable data: trained on classical music data)"]}], 'duration': 759.114, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo1754367.jpg', 'highlights': ['The LSTM is effective at tracking long-term dependencies by controlling the flow of information through gates, largely eliminating the vanishing gradient problem', 'The backpropagation through time algorithm involves backpropagating errors through individual time steps to gradually adjust the weights of the network', 'Training RNNs involves using the backpropagation algorithm with a twist to handle sequential information and update the loss with respect to each network parameter', 'An example of RNN application is in music generation, where an RNN can predict the next musical note in a sequence and generate brand new musical sequences', "The RNN was trained on a corpus of classical music data to generate the third movement to Schubert's famous Unfinished Symphony, showcasing the quality of the music generated", 'Changing the activation functions, particularly using ReLU, can effectively mitigate the vanishing gradient problem by ensuring that the derivative is not less than one in instances where x is greater than zero', 'Adjusting the parameter initialization by initializing weights to identity matrices can help prevent the weight updates from shrinking too rapidly to zero, thus addressing the vanishing gradient problem', 'The exploding gradient problem occurs when weight matrices are very large, leading to unstable network training due to excessively large gradients, which can be mitigated using gradient clipping to scale back the gradients']}, {'end': 3011.832, 'segs': [{'end': 2680.959, 'src': 'embed', 'start': 2638.792, 'weight': 1, 'content': [{'end': 2642.938, 'text': 'In practice, this is very, very challenging and a lot of information can be lost.', 'start': 2638.792, 'duration': 4.146}, {'end': 2650.149, 'text': 'Another limitation is that by doing this time step by time step processing, RNNs can be quite slow.', 'start': 2644.14, 'duration': 6.009}, {'end': 2653.975, 'text': "There's not really an easy way to parallelize that computation.", 'start': 2650.77, 'duration': 3.205}, {'end': 2659.769, 'text': 'And finally together these components of the encoding bottleneck.', 'start': 2655.467, 'duration': 4.302}, {'end': 2668.532, 'text': 'the requirement to process this data step by step imposes the biggest problem, which is when we talk about long memory,', 'start': 2659.769, 'duration': 8.763}, {'end': 2672.514, 'text': 'the capacity of the RNN and the LSTM is really not that long.', 'start': 2668.532, 'duration': 3.982}, {'end': 2680.959, 'text': "We can't really handle data of tens of thousands or hundreds of thousands, or even beyond sequential information,", 'start': 2673.234, 'duration': 7.725}], 'summary': 'Rnns face challenges in slow processing and limited capacity for long memory data.', 'duration': 42.167, 'max_score': 2638.792, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo2638792.jpg'}, {'end': 2820.531, 'src': 'embed', 'start': 2797.932, 'weight': 6, 'content': [{'end': 2809.942, 'text': "Well, a first and naive approach would be to just squash all the data all the time steps together to create a vector that's effectively concatenated right?", 'start': 2797.932, 'duration': 12.01}, {'end': 2811.263, 'text': 'The time steps are eliminated.', 'start': 2810.002, 'duration': 1.261}, {'end': 2820.531, 'text': "There's just one stream where we have now one vector input with the data from all time points that's then fed into the model.", 'start': 2811.284, 'duration': 9.247}], 'summary': 'Concatenate all time steps into one vector input for the model', 'duration': 22.599, 'max_score': 2797.932, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo2797932.jpg'}, {'end': 2865.721, 'src': 'embed', 'start': 2841.415, 'weight': 7, 'content': [{'end': 2850.633, 'text': "but we still have the issues that It's not scalable because the dense feed-forward network would have to be immensely large, defined by many,", 'start': 2841.415, 'duration': 9.218}, {'end': 2851.653, 'text': 'many different connections.', 'start': 2850.633, 'duration': 1.02}, {'end': 2858.317, 'text': "And critically, we've completely lost our in-order information by just squashing everything together blindly.", 'start': 2852.634, 'duration': 5.683}, {'end': 2865.721, 'text': "There's no temporal dependence, and we're then stuck in our ability to try to establish long-term memory.", 'start': 2858.857, 'duration': 6.864}], 'summary': 'Feed-forward network lacks scalability, loses in-order information, and hinders long-term memory.', 'duration': 24.306, 'max_score': 2841.415, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo2841415.jpg'}, {'end': 2914.198, 'src': 'embed', 'start': 2891.471, 'weight': 3, 'content': [{'end': 2899.356, 'text': 'And this is the notion of attention or self-attention, which is an extremely, extremely powerful concept in modern deep learning and AI.', 'start': 2891.471, 'duration': 7.885}, {'end': 2902.698, 'text': "I cannot understate or, I don't know, understate, overstate.", 'start': 2899.736, 'duration': 2.962}, {'end': 2906.261, 'text': 'I cannot emphasize enough how powerful this concept is.', 'start': 2902.718, 'duration': 3.543}, {'end': 2914.198, 'text': 'Attention is the foundational mechanism of the transformer architecture, which many of you may have heard about.', 'start': 2907.651, 'duration': 6.547}], 'summary': 'Self-attention is a powerful concept in deep learning and ai, foundational in transformer architecture.', 'duration': 22.727, 'max_score': 2891.471, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo2891471.jpg'}, {'end': 2965.09, 'src': 'embed', 'start': 2938.074, 'weight': 0, 'content': [{'end': 2945.437, 'text': "break it down step by step to see why it's so powerful and how we can use it as part of a larger neural network like a transformer.", 'start': 2938.074, 'duration': 7.363}, {'end': 2956.762, 'text': "Specifically, we're going to be talking and focusing on this idea of self-attention, attending to the most important parts of an input example.", 'start': 2947.634, 'duration': 9.128}, {'end': 2959.925, 'text': "So let's consider an image.", 'start': 2957.583, 'duration': 2.342}, {'end': 2963.268, 'text': "I think it's most intuitive to consider an image first.", 'start': 2960.226, 'duration': 3.042}, {'end': 2965.09, 'text': 'This is a picture of Iron Man.', 'start': 2963.909, 'duration': 1.181}], 'summary': 'Exploring the power of self-attention in neural networks, focusing on attending to important parts of an input example.', 'duration': 27.016, 'max_score': 2938.074, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo2938074.jpg'}], 'start': 2513.541, 'title': 'Challenges in rnn and self-attention', 'summary': 'Discusses the limitations of rnns such as encoding bottleneck, slow processing, and inability to handle long memory. it also explores the concept of self-attention in modern deep learning, highlighting its role in the transformer architecture and identifying important input parts.', 'chapters': [{'end': 2774.751, 'start': 2513.541, 'title': 'Challenges of rnn in sequence modeling', 'summary': 'Discusses the limitations of rnns, highlighting the encoding bottleneck, slow processing, and the inability to handle long memory, leading to the need for more powerful architectures for processing sequential data.', 'duration': 261.21, 'highlights': ['RNNs have limitations such as encoding bottleneck, slow processing, and inability to handle long memory.', 'The encoding bottleneck in RNNs poses challenges in maintaining and learning sequential information, leading to potential loss of information.', 'RNNs are slow due to the sequential time step processing, with no easy parallelization of computation.', 'RNNs struggle to handle long memory, limiting their capacity to effectively learn from rich sequential data sources.']}, {'end': 2865.721, 'start': 2776.257, 'title': 'Challenges of rnns', 'summary': 'Discusses the limitations of rnns due to their time step processing and explores the possibility of eliminating recurrence entirely to process data, highlighting the potential issues of eliminating time steps and the challenges of establishing long-term memory.', 'duration': 89.464, 'highlights': ['Eliminating time steps by squashing all data together results in a single vector input, but it leads to a loss of temporal dependence and the inability to establish long-term memory.', 'The dense feed-forward network as an alternative to recurrence is not scalable due to the need for a large number of connections.']}, {'end': 3011.832, 'start': 2868.191, 'title': 'Self-attention in deep learning', 'summary': 'Discusses the concept of attention in modern deep learning and ai, emphasizing its powerful role as the foundational mechanism of the transformer architecture and how it is used to identify and attend to important parts of an input example.', 'duration': 143.641, 'highlights': ['Attention is the foundational mechanism of the transformer architecture in deep learning and AI.', 'Self-attention is a powerful concept used to identify and attend to important parts of an input example.', 'The chapter breaks down the concept of attention step by step to demonstrate its power and usage in larger neural networks like a transformer.', 'Illustrating the concept of self-attention using the example of attending to important parts of an image, such as identifying Iron Man in a picture.']}], 'duration': 498.291, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo2513541.jpg', 'highlights': ['Self-attention is a powerful concept used to identify and attend to important parts of an input example.', 'The encoding bottleneck in RNNs poses challenges in maintaining and learning sequential information, leading to potential loss of information.', 'RNNs have limitations such as encoding bottleneck, slow processing, and inability to handle long memory.', 'Attention is the foundational mechanism of the transformer architecture in deep learning and AI.', 'RNNs struggle to handle long memory, limiting their capacity to effectively learn from rich sequential data sources.', 'RNNs are slow due to the sequential time step processing, with no easy parallelization of computation.', 'Eliminating time steps by squashing all data together results in a single vector input, but it leads to a loss of temporal dependence and the inability to establish long-term memory.', 'The dense feed-forward network as an alternative to recurrence is not scalable due to the need for a large number of connections.', 'The chapter breaks down the concept of attention step by step to demonstrate its power and usage in larger neural networks like a transformer.', 'Illustrating the concept of self-attention using the example of attending to important parts of an image, such as identifying Iron Man in a picture.']}, {'end': 3380.121, 'segs': [{'end': 3043.747, 'src': 'embed', 'start': 3013.206, 'weight': 3, 'content': [{'end': 3017.468, 'text': 'The first part of this problem is really the most interesting and challenging one.', 'start': 3013.206, 'duration': 4.262}, {'end': 3021.33, 'text': "And it's very similar to the concept of search.", 'start': 3018.229, 'duration': 3.101}, {'end': 3030.014, 'text': "Effectively, that's what search is doing, taking some larger body of information and trying to extract and identify the important parts.", 'start': 3021.79, 'duration': 8.224}, {'end': 3031.895, 'text': "So let's go there next.", 'start': 3030.935, 'duration': 0.96}, {'end': 3032.956, 'text': 'How does search work?', 'start': 3032.175, 'duration': 0.781}, {'end': 3035.381, 'text': "You're thinking you're in this class.", 'start': 3033.8, 'duration': 1.581}, {'end': 3037.082, 'text': 'how can I learn more about neural networks?', 'start': 3035.381, 'duration': 1.701}, {'end': 3043.747, 'text': 'Well, in this day and age, one thing you may do, besides coming here and joining us, is going to the internet,', 'start': 3037.483, 'duration': 6.264}], 'summary': 'Understanding the concept of search and its role in extracting important information from a larger data set.', 'duration': 30.541, 'max_score': 3013.206, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo3013206.jpg'}, {'end': 3324.925, 'src': 'heatmap', 'start': 3112.642, 'weight': 0, 'content': [{'end': 3117.371, 'text': 'The third option, a video about the late and great Kobe Bryant, not that relevant.', 'start': 3112.642, 'duration': 4.729}, {'end': 3123.904, 'text': 'The key operation here is that there is this similarity computation, bringing the query and the key together.', 'start': 3118.113, 'duration': 5.791}, {'end': 3132.978, 'text': "The final step is now that we've identified what key is relevant, extracting the relevant information, what we want to pay attention to.", 'start': 3125.151, 'duration': 7.827}, {'end': 3134.62, 'text': "And that's the video itself.", 'start': 3133.519, 'duration': 1.101}, {'end': 3136.362, 'text': 'We call this the value.', 'start': 3135.261, 'duration': 1.101}, {'end': 3139.785, 'text': 'And because the search is implemented well,', 'start': 3136.982, 'duration': 2.803}, {'end': 3145.11, 'text': "we've successfully identified the relevant video on deep learning that you are going to want to pay attention to.", 'start': 3139.785, 'duration': 5.325}, {'end': 3151.856, 'text': "And it's this idea, this intuition of giving a query, trying to find similarity,", 'start': 3146.311, 'duration': 5.545}, {'end': 3159.783, 'text': 'trying to extract the related values that form the basis of self-attention and how it works in neural networks like transformers.', 'start': 3151.856, 'duration': 7.927}, {'end': 3166.67, 'text': "So to go concretely into this, right, let's go back now to our text, our language example.", 'start': 3160.584, 'duration': 6.086}, {'end': 3177.265, 'text': 'With the sentence, Our goal is to identify and attend to features in this input that are relevant to the semantic meaning of the sentence.', 'start': 3167.931, 'duration': 9.334}, {'end': 3187.411, 'text': "Now, first step, we have sequence, we have order, we've eliminated recurrence, right? We're feeding in all the time steps all at once.", 'start': 3178.826, 'duration': 8.585}, {'end': 3193.915, 'text': 'We still need a way to encode and capture this information about order and this positional dependence.', 'start': 3188.131, 'duration': 5.784}, {'end': 3204.101, 'text': 'How this is done is this idea of positional encoding, which captures some inherent order information present in the sequence.', 'start': 3194.975, 'duration': 9.126}, {'end': 3211.025, 'text': "I'm just going to touch on this very briefly, but the idea is related to this idea of embeddings, which I introduced earlier.", 'start': 3204.761, 'duration': 6.264}, {'end': 3223.733, 'text': 'What is done is a neural network layer is used to encode positional information that captures the relative relationships in terms of order within this text.', 'start': 3212.226, 'duration': 11.507}, {'end': 3231.547, 'text': "That's the high level concept, right? We're still being able to process these time steps all at once.", 'start': 3225.558, 'duration': 5.989}, {'end': 3233.43, 'text': 'There is no notion of time step, rather.', 'start': 3231.948, 'duration': 1.482}, {'end': 3234.492, 'text': 'The data is singular.', 'start': 3233.45, 'duration': 1.042}, {'end': 3239.92, 'text': 'But still, we learn this encoding that captures the positional order information.', 'start': 3235.052, 'duration': 4.868}, {'end': 3245.892, 'text': 'Now our next step is to take this encoding and figure out what to attend to,', 'start': 3241.249, 'duration': 4.643}, {'end': 3249.835, 'text': 'exactly like that search operation that I introduced with the YouTube example.', 'start': 3245.892, 'duration': 3.943}, {'end': 3255.258, 'text': 'Extracting a query, extracting a key, extracting a value, and relating them to each other.', 'start': 3250.575, 'duration': 4.683}, {'end': 3259.201, 'text': 'So we use neural network layers to do exactly this.', 'start': 3256.059, 'duration': 3.142}, {'end': 3269.088, 'text': 'Given this positional encoding, what attention does is applies a neural network layer, transforming that, first generating the query.', 'start': 3259.962, 'duration': 9.126}, {'end': 3273.82, 'text': 'We do this, again, using a separate neural network layer.', 'start': 3270.558, 'duration': 3.262}, {'end': 3281.325, 'text': 'And this is a different set of weights, a different set of parameters that then transform that positional embedding in a different way,', 'start': 3274.42, 'duration': 6.905}, {'end': 3284.607, 'text': 'generating a second output, the key.', 'start': 3281.325, 'duration': 3.282}, {'end': 3292.412, 'text': 'And finally, this operation is repeated with a third layer, a third set of weights generating the value.', 'start': 3285.507, 'duration': 6.905}, {'end': 3303.786, 'text': 'Now, with these three in hand the query, the key and the value we can compare them to each other to try to figure out where, in that self-input,', 'start': 3293.419, 'duration': 10.367}, {'end': 3306.048, 'text': 'the network should attend to what is important.', 'start': 3303.786, 'duration': 2.262}, {'end': 3313.133, 'text': "And that's the key idea behind this similarity metric, or what you can think of as an attention score.", 'start': 3307.249, 'duration': 5.884}, {'end': 3318.797, 'text': "What we're doing is we're computing a similarity score between a query and the key.", 'start': 3313.873, 'duration': 4.924}, {'end': 3324.925, 'text': 'And remember that these query and key values are just arrays of numbers.', 'start': 3319.901, 'duration': 5.024}], 'summary': 'Neural networks use self-attention to extract relevant information, like in the example of identifying a relevant video on deep learning, by computing similarity scores between queries and keys.', 'duration': 212.283, 'max_score': 3112.642, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo3112642.jpg'}, {'end': 3234.492, 'src': 'embed', 'start': 3212.226, 'weight': 1, 'content': [{'end': 3223.733, 'text': 'What is done is a neural network layer is used to encode positional information that captures the relative relationships in terms of order within this text.', 'start': 3212.226, 'duration': 11.507}, {'end': 3231.547, 'text': "That's the high level concept, right? We're still being able to process these time steps all at once.", 'start': 3225.558, 'duration': 5.989}, {'end': 3233.43, 'text': 'There is no notion of time step, rather.', 'start': 3231.948, 'duration': 1.482}, {'end': 3234.492, 'text': 'The data is singular.', 'start': 3233.45, 'duration': 1.042}], 'summary': 'A neural network encodes positional information to process time steps all at once in singular data.', 'duration': 22.266, 'max_score': 3212.226, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo3212226.jpg'}, {'end': 3357.597, 'src': 'embed', 'start': 3332.111, 'weight': 2, 'content': [{'end': 3338.332, 'text': 'The query values are some vector, the key, the key values are some other vector.', 'start': 3332.111, 'duration': 6.221}, {'end': 3347.805, 'text': 'And mathematically, the way that we can compare these two vectors to understand how similar they are is by taking the dot product and scaling it.', 'start': 3339.053, 'duration': 8.752}, {'end': 3357.597, 'text': "Captures how similar these vectors are, whether or not they're pointing in the same direction, right? This is the similarity metric.", 'start': 3348.145, 'duration': 9.452}], 'summary': 'Comparing vectors using dot product to measure similarity.', 'duration': 25.486, 'max_score': 3332.111, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo3332111.jpg'}], 'start': 3013.206, 'title': 'Neural network attention mechanism', 'summary': 'Delves into how search and self-attention work in neural networks, using youtube video search as an example, and explains the concept of positional encoding and attention mechanism in neural networks, along with the process of similarity computation and value extraction.', 'chapters': [{'end': 3159.783, 'start': 3013.206, 'title': 'Search and self-attention in neural networks', 'summary': 'Explains how search works in extracting relevant information from a large database, using the example of finding videos on youtube, and highlights the process of similarity computation and value extraction, forming the basis of self-attention in neural networks like transformers.', 'duration': 146.577, 'highlights': ["The process of similarity computation and relevance identification between the query and database keys is explained, with examples of relevant and irrelevant matches such as 'deep learning' and 'Kobe Bryant'.", "The concept of search in extracting and identifying important parts from a large body of information is likened to the process of finding videos on YouTube, where the relevant video on 'deep learning' is identified through the search operation.", "The task of extracting relevant information, termed as the 'value', is highlighted as the final step of the search process, emphasizing the successful identification of the relevant video on deep learning."]}, {'end': 3380.121, 'start': 3160.584, 'title': 'Neural network attention mechanism', 'summary': 'Explains the concept of positional encoding and attention mechanism in neural networks, which involves using neural network layers to encode positional information and applying a similarity metric to compare query and key vectors.', 'duration': 219.537, 'highlights': ['Neural network layers are used to encode positional information that captures the relative relationships in terms of order within the text.', 'Applying a similarity metric involves computing a similarity score between a query and the key by taking the dot product and scaling it, which captures how similar these vectors are.', 'The operation functions similarly for matrices, and the dot product operation applied to query and key matrices yields the similarity metric.']}], 'duration': 366.915, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo3013206.jpg', 'highlights': ["The process of similarity computation and relevance identification between the query and database keys is explained, with examples of relevant and irrelevant matches such as 'deep learning' and 'Kobe Bryant'.", 'Neural network layers are used to encode positional information that captures the relative relationships in terms of order within the text.', 'Applying a similarity metric involves computing a similarity score between a query and the key by taking the dot product and scaling it, which captures how similar these vectors are.', "The concept of search in extracting and identifying important parts from a large body of information is likened to the process of finding videos on YouTube, where the relevant video on 'deep learning' is identified through the search operation.", "The task of extracting relevant information, termed as the 'value', is highlighted as the final step of the search process, emphasizing the successful identification of the relevant video on deep learning."]}, {'end': 3767.507, 'segs': [{'end': 3434.179, 'src': 'embed', 'start': 3380.121, 'weight': 2, 'content': [{'end': 3389.068, 'text': 'very key in defining our next step computing the attention waiting in terms of what the network should actually attend to within this input.', 'start': 3380.121, 'duration': 8.947}, {'end': 3400.516, 'text': 'This operation gives us a score which defines how the components of the input data are related to each other.', 'start': 3390.609, 'duration': 9.907}, {'end': 3403.46, 'text': 'So given a sentence right.', 'start': 3401.758, 'duration': 1.702}, {'end': 3407.044, 'text': 'when we compute this similarity score metric,', 'start': 3403.46, 'duration': 3.584}, {'end': 3416.034, 'text': 'we can then begin to think of weights that define the relationship between the components of the sequential data to each other.', 'start': 3407.044, 'duration': 8.99}, {'end': 3422.962, 'text': 'So, for example, in this example, with a text sentence, he tossed the tennis ball to serve.', 'start': 3416.875, 'duration': 6.087}, {'end': 3430.535, 'text': 'The goal with the score is that words in the sequence that are related to each other should have high attention.', 'start': 3424.249, 'duration': 6.286}, {'end': 3430.795, 'text': 'weights.', 'start': 3430.535, 'duration': 0.26}, {'end': 3434.179, 'text': 'Ball related to toss, related to tennis.', 'start': 3431.396, 'duration': 2.783}], 'summary': 'Computing attention scores to define relationships in sequential data.', 'duration': 54.058, 'max_score': 3380.121, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo3380121.jpg'}, {'end': 3579.391, 'src': 'embed', 'start': 3551.823, 'weight': 1, 'content': [{'end': 3561.231, 'text': 'What is so powerful about this approach, in taking this attention weight, putting it together with the value to extract high attention features,', 'start': 3551.823, 'duration': 9.408}, {'end': 3567.416, 'text': "is that this operation, this scheme that I'm showing on the right, defines a single self-attention head.", 'start': 3561.231, 'duration': 6.185}, {'end': 3579.391, 'text': 'And multiple of these self-attention heads can be linked together to form larger network architectures where you can think about these different heads trying to extract different information,', 'start': 3568.257, 'duration': 11.134}], 'summary': 'Self-attention heads extract high attention features, forming larger network architectures.', 'duration': 27.568, 'max_score': 3551.823, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo3551823.jpg'}, {'end': 3656.16, 'src': 'embed', 'start': 3632.543, 'weight': 0, 'content': [{'end': 3645.452, 'text': 'And indeed this backbone idea of self-attention that you just built up understanding of is the key operation of some of the most powerful neural networks and deep learning models out there today,', 'start': 3632.543, 'duration': 12.909}, {'end': 3656.16, 'text': 'ranging from the very powerful language models like GPT-3,, which are capable of synthesizing natural language in a very human-like fashion,', 'start': 3645.452, 'duration': 10.708}], 'summary': 'Self-attention is a key operation in powerful neural networks like gpt-3.', 'duration': 23.617, 'max_score': 3632.543, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo3632543.jpg'}], 'start': 3380.121, 'title': 'Self-attention mechanism in nlp', 'summary': 'Explains self-attention in nlp, detailing its use in attending to important features, eliminating recurrence, and building powerful models. it covers computing attention weights, extracting high-attention features, and its impact in neural networks and deep learning models.', 'chapters': [{'end': 3434.179, 'start': 3380.121, 'title': 'Computing attention weights in nlp', 'summary': 'Focuses on computing attention weights in nlp, which involves defining how the components of input data are related, leading to the computation of similarity scores and the determination of weights that define the relationship between the components of sequential data.', 'duration': 54.058, 'highlights': ['Computing attention weights involves defining the relationship between components of input data, leading to the computation of similarity scores.', "The goal is to have high attention weights for words in the sequence that are related to each other, such as 'ball' related to 'toss' and 'tennis'.", 'The operation gives a score that defines how the components of the input data are related to each other.']}, {'end': 3767.507, 'start': 3435.2, 'title': 'Self-attention mechanism in neural networks', 'summary': 'Explains the concept of self-attention, which is used to attend to important features in input data, eliminating recurrence and building powerful models. it details the process of computing attention weights, extracting high-attention features, and the impact of self-attention in neural networks and deep learning models.', 'duration': 332.307, 'highlights': ['Self-attention mechanism is used to attend to important features in input data, eliminating recurrence and building powerful models.', 'The process involves computing attention weights, extracting high-attention features, and using multiple self-attention heads to build rich representations of complex data.', 'Self-attention has a significant impact on neural networks and deep learning models, being a key operation in powerful models like GPT-3 and AlphaFold2, and transforming the field of computer vision.']}], 'duration': 387.386, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/ySEx_Bqxvvo/pics/ySEx_Bqxvvo3380121.jpg', 'highlights': ['Self-attention has a significant impact on neural networks and deep learning models, being a key operation in powerful models like GPT-3 and AlphaFold2, and transforming the field of computer vision.', 'The process involves computing attention weights, extracting high-attention features, and using multiple self-attention heads to build rich representations of complex data.', 'Computing attention weights involves defining the relationship between components of input data, leading to the computation of similarity scores.', "The goal is to have high attention weights for words in the sequence that are related to each other, such as 'ball' related to 'toss' and 'tennis'.", 'The operation gives a score that defines how the components of the input data are related to each other.']}], 'highlights': ['Self-attention is a powerful concept used to identify and attend to important parts of an input example.', 'The LSTM is effective at tracking long-term dependencies by controlling the flow of information through gates, largely eliminating the vanishing gradient problem', 'The process involves computing attention weights, extracting high-attention features, and using multiple self-attention heads to build rich representations of complex data.', "The process of similarity computation and relevance identification between the query and database keys is explained, with examples of relevant and irrelevant matches such as 'deep learning' and 'Kobe Bryant'.", 'The significance of prior information in making predictions is emphasized using the example of predicting the trajectory of a ball, where the addition of historical motion information makes the prediction task easier.']}