title
MIT 6.S191 (2020): Recurrent Neural Networks

description
MIT Introduction to Deep Learning 6.S191: Lecture 2 Recurrent Neural Networks Lecturer: Ava Soleimany January 2020 For all lectures, slides, and lab materials: http://introtodeeplearning.com Lecture Outline 0:00 - Introduction 2:39 - Sequence modeling 9:57 - Recurrent neural networks 14:04 - RNN intuition 16:48 - Unfolding RNNs 20:31 - Backpropagation through time 24:32 - Gradient issues 28:57 - Long short term memory (LSTM) 37:36 - RNN applications 41:30 - Attention 44:05 - Summary Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

detail
{'title': 'MIT 6.S191 (2020): Recurrent Neural Networks', 'heatmap': [{'end': 1711.131, 'start': 1659.382, 'weight': 0.703}, {'end': 2019.912, 'start': 1960.08, 'weight': 0.788}], 'summary': 'Delves into the significance of sequential data in various applications, challenges in handling variable length inputs, sequence modeling design criteria, recurrent neural networks (rnns) for handling sequential data, addressing issues of exploding and vanishing gradients in rnn training, solutions to vanishing gradient problem, understanding long short-term memory (lstm) networks, lstm computation process, and applications of lstms and rnns in music generation, sentiment analysis, language translation, autonomous vehicles, environmental modeling, and more.', 'chapters': [{'end': 523.595, 'segs': [{'end': 151.477, 'src': 'embed', 'start': 94.788, 'weight': 0, 'content': [{'end': 102.231, 'text': 'And I think we can all agree that we have a very clear sense of where this ball is likely to travel to next.', 'start': 94.788, 'duration': 7.443}, {'end': 111.173, 'text': 'And so this is really to kind of set up this idea of processing and handling sequential data.', 'start': 103.41, 'duration': 7.763}, {'end': 116.615, 'text': 'And in truth, if you consider it, sequential data is all around us.', 'start': 111.873, 'duration': 4.742}, {'end': 121.717, 'text': 'For example, audio can be split up into a sequence of sound waves.', 'start': 117.095, 'duration': 4.622}, {'end': 128.46, 'text': 'And text can be split up into sequences of either characters or words.', 'start': 122.918, 'duration': 5.542}, {'end': 140.211, 'text': 'And beyond these two ubiquitous examples that we encounter every day, there are many more cases in which sequential processing may be useful,', 'start': 130.185, 'duration': 10.026}, {'end': 151.477, 'text': 'from analyzing medical signals like EEGs to projecting stock prices, to inferring and understanding genomic sequences.', 'start': 140.211, 'duration': 11.266}], 'summary': 'Processing sequential data is crucial, as seen in audio, text, medical signals, and stock prices.', 'duration': 56.689, 'max_score': 94.788, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU94788.jpg'}, {'end': 225.6, 'src': 'embed', 'start': 202.026, 'weight': 2, 'content': [{'end': 209.151, 'text': "And since we're here to learn about deep learning and this is a class on deep learning let's say we want to build a deep neural network,", 'start': 202.026, 'duration': 7.125}, {'end': 212.993, 'text': 'like a feed-forward neural network from lecture one, to do exactly this.', 'start': 209.151, 'duration': 3.842}, {'end': 225.6, 'text': 'And one problem that we will very immediately run into is that This feed-forward network can only take a fixed length input vector as its input.', 'start': 214.474, 'duration': 11.126}], 'summary': 'Learning about deep learning, aiming to build a feed-forward neural network for fixed length input vector.', 'duration': 23.574, 'max_score': 202.026, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU202026.jpg'}, {'end': 335.489, 'src': 'embed', 'start': 305.58, 'weight': 3, 'content': [{'end': 310.625, 'text': "And that's because, since we're using this fixed window of only two words,", 'start': 305.58, 'duration': 5.045}, {'end': 318.692, 'text': "we're giving ourselves a very limited history in trying to solve this problem of being able to predict the next word in the sentence.", 'start': 310.625, 'duration': 8.067}, {'end': 327.681, 'text': 'And that means that we cannot effectively model long-term dependencies, which is really important in sentences like this one.', 'start': 319.813, 'duration': 7.868}, {'end': 335.489, 'text': 'where we clearly need information from much earlier in the sentence to be able to accurately predict the next word.', 'start': 328.905, 'duration': 6.584}], 'summary': 'Using a fixed window of two words limits the ability to model long-term dependencies in sentence prediction.', 'duration': 29.909, 'max_score': 305.58, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU305580.jpg'}, {'end': 402.734, 'src': 'embed', 'start': 372.906, 'weight': 4, 'content': [{'end': 381.345, 'text': 'And this representation is commonly called a bag of words, where now each slot in our input vector represents a word.', 'start': 372.906, 'duration': 8.439}, {'end': 388.508, 'text': 'And the value in that slot represents the number of times that that word appears in our sentence.', 'start': 382.745, 'duration': 5.763}, {'end': 395.791, 'text': 'And so here, we have a fixed length vector, regardless of the identity of the sentence.', 'start': 389.348, 'duration': 6.443}, {'end': 402.734, 'text': 'But what differs sentence to sentence is how the counts over this vocabulary change.', 'start': 396.291, 'duration': 6.443}], 'summary': 'Bag of words represents word frequency in fixed-length vectors.', 'duration': 29.828, 'max_score': 372.906, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU372906.jpg'}], 'start': 2.895, 'title': 'Deep sequence modeling and challenges in handling variable length inputs', 'summary': 'Discusses the significance of sequential data in various applications such as audio and text processing, medical signals, and stock price projection. it also highlights the challenges in handling variable length inputs in deep neural network building, including limitations of fixed window approach and bag of words representation.', 'chapters': [{'end': 200.16, 'start': 2.895, 'title': 'Deep sequence modeling', 'summary': 'Discusses the need for processing sequential data, giving examples such as predicting the trajectory of a ball and predicting the next word in a sentence, and highlights the importance of sequential data in various applications, from audio and text processing to medical signals and stock price projection.', 'duration': 197.265, 'highlights': ['Sequential data is all around us, from audio being split up into a sequence of sound waves to text being split up into sequences of characters or words.', 'Examples of applications for sequential processing include analyzing medical signals like EEGs, projecting stock prices, and inferring and understanding genomic sequences.', 'The chapter sets up the idea of processing and handling sequential data by giving a simple example of predicting the trajectory of a ball and the question of predicting the next word in a sentence.']}, {'end': 523.595, 'start': 202.026, 'title': 'Challenges in handling variable length inputs', 'summary': 'Discusses the challenges of handling variable length inputs in building a deep neural network, including the limitations of using a fixed window approach and the issues with bag of words representation, as well as the shortcomings of extending the fixed window in capturing sequential information.', 'duration': 321.569, 'highlights': ['The feed-forward network can only take a fixed length input vector, creating a challenge in handling variable length inputs.', "Using a fixed window approach restricts the model's ability to effectively model long-term dependencies, impacting the accuracy of predictions.", 'Representing the sequence as a bag of words results in the loss of sequential information and prior history, leading to inaccurate representations of sentences with different semantic meanings.']}], 'duration': 520.7, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU2895.jpg', 'highlights': ['Examples of applications for sequential processing include analyzing medical signals like EEGs, projecting stock prices, and inferring and understanding genomic sequences.', 'Sequential data is all around us, from audio being split up into a sequence of sound waves to text being split up into sequences of characters or words.', 'The feed-forward network can only take a fixed length input vector, creating a challenge in handling variable length inputs.', "Using a fixed window approach restricts the model's ability to effectively model long-term dependencies, impacting the accuracy of predictions.", 'Representing the sequence as a bag of words results in the loss of sequential information and prior history, leading to inaccurate representations of sentences with different semantic meanings.']}, {'end': 1326.937, 'segs': [{'end': 567.987, 'src': 'embed', 'start': 524.376, 'weight': 1, 'content': [{'end': 538.608, 'text': 'And so this really motivates this need for being able to track long-term dependencies and have parameters that can be shared across the entirety of our sequence.', 'start': 524.376, 'duration': 14.232}, {'end': 555.623, 'text': "And so I hope that this simple example of having a sentence where we're trying to predict the next word motivates sort of a concrete set of design criteria that we need to be keeping in mind when we're thinking about sequence modeling problems.", 'start': 539.758, 'duration': 15.865}, {'end': 567.987, 'text': 'Specifically, we need to develop models that can handle variable length input sequences, that are able to track long-term dependencies in the data.', 'start': 556.743, 'duration': 11.244}], 'summary': 'Need for tracking long-term dependencies in sequence modeling.', 'duration': 43.611, 'max_score': 524.376, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU524376.jpg'}, {'end': 678.078, 'src': 'embed', 'start': 646.652, 'weight': 0, 'content': [{'end': 651.075, 'text': "And they're great for problems in which a sequence of data is being propagated.", 'start': 646.652, 'duration': 4.423}, {'end': 653.688, 'text': 'to give a single output.', 'start': 652.287, 'duration': 1.401}, {'end': 654.848, 'text': 'So, for example,', 'start': 654.088, 'duration': 0.76}, {'end': 664.032, 'text': "we can imagine where we're training a model that takes as input a sequence of words and outputs a sentiment or an emotion associated with that sequence.", 'start': 654.848, 'duration': 9.184}, {'end': 672.075, 'text': 'We can also consider cases where, instead of returning a single output, we could have a sequence of inputs,', 'start': 665.332, 'duration': 6.743}, {'end': 678.078, 'text': 'propagate them through our network and then, at each time, step in the sequence, generate an output.', 'start': 672.075, 'duration': 6.003}], 'summary': 'Neural networks are useful for processing sequential data to generate single or multiple outputs.', 'duration': 31.426, 'max_score': 646.652, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU646652.jpg'}, {'end': 812.27, 'src': 'embed', 'start': 780.312, 'weight': 4, 'content': [{'end': 786.577, 'text': "And so what's going on, sort of under the hood of this network, and how is information actually being passed?", 'start': 780.312, 'duration': 6.265}, {'end': 787.638, 'text': 'time, step to time, step?', 'start': 786.577, 'duration': 1.061}, {'end': 795.704, 'text': "The way that's done is by using a simple recurrence relation to process the sequential data.", 'start': 789.679, 'duration': 6.025}, {'end': 802.482, 'text': 'Specifically, RNNs maintain this internal state, h of t.', 'start': 797.037, 'duration': 5.445}, {'end': 812.27, 'text': "And at each time step, they apply a function, f, that's parameterized by a set of weights, w, to update the state, h.", 'start': 802.482, 'duration': 9.788}], 'summary': 'Rnns process sequential data using recurrence relation, updating state at each time step.', 'duration': 31.958, 'max_score': 780.312, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU780312.jpg'}, {'end': 892.301, 'src': 'embed', 'start': 857.796, 'weight': 5, 'content': [{'end': 861.881, 'text': 'And so this pseudocode kind of breaks it down into a few simple steps.', 'start': 857.796, 'duration': 4.085}, {'end': 868.81, 'text': 'We begin by initializing our RNN, and we also initialize the hidden state of that network.', 'start': 862.762, 'duration': 6.048}, {'end': 874.558, 'text': 'And we can denote a sentence for which we are interested in predicting the next word.', 'start': 869.691, 'duration': 4.867}, {'end': 881.639, 'text': 'The RNN computation simply consists of then looping through the words in the sentence.', 'start': 876.598, 'duration': 5.041}, {'end': 892.301, 'text': "And at each time step we feed both the current word that we're considering as well as the previous hidden state of our RNN into the network,", 'start': 882.419, 'duration': 9.882}], 'summary': 'A pseudocode outlines steps for initializing, computing, and predicting with an rnn.', 'duration': 34.505, 'max_score': 857.796, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU857796.jpg'}, {'end': 1326.937, 'src': 'embed', 'start': 1303.579, 'weight': 6, 'content': [{'end': 1311.723, 'text': 'And so that means that instead of back-propagating errors through a single feed-forward network at a single time step in RNNs,', 'start': 1303.579, 'duration': 8.144}, {'end': 1321.607, 'text': 'errors are back-propagated at each individual time step and then finally across all time steps all the way from where we are currently to the beginning of the sequence.', 'start': 1311.723, 'duration': 9.884}, {'end': 1326.937, 'text': "And this is the reason why it's called back-propagation through time.", 'start': 1322.288, 'duration': 4.649}], 'summary': 'Back-propagates errors at each time step in rnns.', 'duration': 23.358, 'max_score': 1303.579, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU1303579.jpg'}], 'start': 524.376, 'title': 'Sequence modeling and rnns', 'summary': 'Discusses sequence modeling design criteria, emphasizing the need to handle variable length input sequences and long-term dependencies. it also explores recurrent neural networks (rnns) as a paradigm for sequential processing, highlighting their ability to maintain information over time and handle sequential data. additionally, it delves into the rnn algorithm, internal state updates, unrolling the network over time, and the back-propagation through time algorithm for training rnns.', 'chapters': [{'end': 567.987, 'start': 524.376, 'title': 'Sequence modeling design criteria', 'summary': 'Discusses the need for sequence models to handle variable length input sequences and track long-term dependencies in data, emphasizing the importance of shared parameters.', 'duration': 43.611, 'highlights': ['Sequence models need to handle variable length input sequences and track long-term dependencies in the data.', 'The need for being able to track long-term dependencies and have parameters that can be shared across the entirety of the sequence is emphasized.', 'Developing models that can handle variable length input sequences and track long-term dependencies in the data is crucial for sequence modeling.']}, {'end': 1326.937, 'start': 569.355, 'title': 'Recurrent neural networks: sequential processing', 'summary': 'Explores the concept of recurrent neural networks (rnns) as a paradigm for sequential processing, emphasizing their ability to maintain information over time and handle sequential data. it also discusses the rnn algorithm, internal state updates, unrolling the network over time, and the back-propagation through time algorithm for training rnns.', 'duration': 757.582, 'highlights': ['RNNs are well-suited for handling sequential data and maintaining information over time, making them suitable for problems involving sequences of inputs and outputs such as sentiment analysis, text or music generation.', 'RNNs utilize loops to pass information from one time step to the next, allowing information to persist over time, and they apply a simple recurrence relation to process sequential data.', "The RNN algorithm involves initializing the network and hidden state, looping through the input sequence, and updating the hidden state to predict the next word in the sequence, ultimately producing the RNN's output.", 'Back-propagation through time is the algorithm for training RNNs, involving back-propagating errors at each individual time step and across all time steps, enabling adjustments to the parameters to minimize the loss.']}], 'duration': 802.561, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU524376.jpg', 'highlights': ['RNNs are well-suited for handling sequential data and maintaining information over time, making them suitable for problems involving sequences of inputs and outputs such as sentiment analysis, text or music generation.', 'Sequence models need to handle variable length input sequences and track long-term dependencies in the data.', 'The need for being able to track long-term dependencies and have parameters that can be shared across the entirety of the sequence is emphasized.', 'Developing models that can handle variable length input sequences and track long-term dependencies in the data is crucial for sequence modeling.', 'RNNs utilize loops to pass information from one time step to the next, allowing information to persist over time, and they apply a simple recurrence relation to process sequential data.', "The RNN algorithm involves initializing the network and hidden state, looping through the input sequence, and updating the hidden state to predict the next word in the sequence, ultimately producing the RNN's output.", 'Back-propagation through time is the algorithm for training RNNs, involving back-propagating errors at each individual time step and across all time steps, enabling adjustments to the parameters to minimize the loss.']}, {'end': 1593.953, 'segs': [{'end': 1409.711, 'src': 'embed', 'start': 1328.017, 'weight': 0, 'content': [{'end': 1334.178, 'text': 'Because as you can see, all the errors are flowing back in time to the beginning of our data sequence.', 'start': 1328.017, 'duration': 6.161}, {'end': 1345.68, 'text': 'And so if we take a closer look at how gradients actually flow across this chain of repeating modules, you can see that between each time step,', 'start': 1335.839, 'duration': 9.841}, {'end': 1352.441, 'text': 'we need to perform a matrix multiplication involving this weight matrix w.', 'start': 1345.68, 'duration': 6.761}, {'end': 1360.665, 'text': 'And remember that this cell update also results from a nonlinear activation function.', 'start': 1352.441, 'duration': 8.224}, {'end': 1372.767, 'text': 'And that means that this computation of the gradient that is the derivative of the loss with respect to the parameters tracing all the way back to our initial state,', 'start': 1361.885, 'duration': 10.882}, {'end': 1382.049, 'text': 'requires many repeated multiplications of this weight matrix, as well as repeated use of the derivative of our activation function.', 'start': 1372.767, 'duration': 9.282}, {'end': 1385.405, 'text': 'And this can be problematic.', 'start': 1383.905, 'duration': 1.5}, {'end': 1393.768, 'text': 'And the reason for that is we can have one of two scenarios that could be particularly problematic.', 'start': 1386.026, 'duration': 7.742}, {'end': 1404.091, 'text': 'If many of these values that are involved in these repeated multiplications, such as the weight matrix or the gradients themselves, are large,', 'start': 1395.468, 'duration': 8.623}, {'end': 1409.711, 'text': "greater than 1, we can run into a problem that's called the exploding gradients problem.", 'start': 1405.188, 'duration': 4.523}], 'summary': 'Repeating multiplications of weight matrix can cause exploding gradients problem', 'duration': 81.694, 'max_score': 1328.017, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU1328017.jpg'}, {'end': 1463.627, 'src': 'embed', 'start': 1437.942, 'weight': 3, 'content': [{'end': 1450.209, 'text': 'And this is what is known as the vanishing gradient problem when gradients become increasingly and increasingly smaller as we make these repeated multiplications and we can no longer train the network.', 'start': 1437.942, 'duration': 12.267}, {'end': 1455.662, 'text': 'And this is a big and very real problem when it comes to training RNNs.', 'start': 1451.4, 'duration': 4.262}, {'end': 1459.945, 'text': "And today we'll go through three ways in which we can address this problem.", 'start': 1456.123, 'duration': 3.822}, {'end': 1463.627, 'text': 'Choosing our activation function,', 'start': 1460.965, 'duration': 2.662}], 'summary': 'Vanishing gradient problem hinders rnn training. three solutions discussed.', 'duration': 25.685, 'max_score': 1437.942, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU1437942.jpg'}], 'start': 1328.017, 'title': 'Backward flow of errors in data sequence and rnn training issues', 'summary': 'Discusses the flow of errors back in time to the beginning of the data sequence, emphasizing repeated matrix multiplications and derivative computations, and also addresses issues of exploding and vanishing gradients in rnn training through gradient clipping, weight initialization, and network architecture design.', 'chapters': [{'end': 1382.049, 'start': 1328.017, 'title': 'Backward flow of errors in data sequence', 'summary': 'Discusses the flow of errors back in time to the beginning of the data sequence, emphasizing the repeated matrix multiplications and derivative computations needed for tracing back to the initial state.', 'duration': 54.032, 'highlights': ['The flow of errors is traced back to the beginning of the data sequence, requiring repeated matrix multiplications and derivative computations.', 'Gradients flow across the chain of repeating modules, involving matrix multiplications and activation function derivatives between each time step.', 'Cell update results from a nonlinear activation function, necessitating repeated multiplications of the weight matrix and the derivative of the activation function.']}, {'end': 1593.953, 'start': 1383.905, 'title': 'Rnn training issues', 'summary': 'Discusses the issues of exploding and vanishing gradients in rnn training, and addresses them through gradient clipping, clever weight initialization, and efficient network architecture design.', 'duration': 210.048, 'highlights': ['Exploding gradients problem can occur when weight matrix or gradients are large, leading to optimization issues.', 'Vanishing gradient problem arises when gradients become increasingly smaller, hindering network training.', 'Three ways to address vanishing gradient issue: choosing activation function, clever weight initialization, and designing efficient network architecture.']}], 'duration': 265.936, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU1328017.jpg', 'highlights': ['Gradients flow across the chain of repeating modules, involving matrix multiplications and activation function derivatives between each time step.', 'The flow of errors is traced back to the beginning of the data sequence, requiring repeated matrix multiplications and derivative computations.', 'Cell update results from a nonlinear activation function, necessitating repeated multiplications of the weight matrix and the derivative of the activation function.', 'Three ways to address vanishing gradient issue: choosing activation function, clever weight initialization, and designing efficient network architecture.', 'Exploding gradients problem can occur when weight matrix or gradients are large, leading to optimization issues.', 'Vanishing gradient problem arises when gradients become increasingly smaller, hindering network training.']}, {'end': 1947.753, 'segs': [{'end': 1711.131, 'src': 'heatmap', 'start': 1652.778, 'weight': 2, 'content': [{'end': 1658.802, 'text': 'And this helps prevent the value of our derivative f prime from shrinking the gradients.', 'start': 1652.778, 'duration': 6.024}, {'end': 1670.829, 'text': 'But keep in mind that this ReLU function only has a gradient of 1 for when x is greater than 0, which is another significant consideration.', 'start': 1659.382, 'duration': 11.447}, {'end': 1678.58, 'text': 'Another simple trick we can do is to be smart in how we initialize the weights, the parameters of our network.', 'start': 1672.614, 'duration': 5.966}, {'end': 1689.271, 'text': 'And it turns out that initializing the weights to the identity matrix helps prevent them from shrinking to zero too rapidly during backpropagation.', 'start': 1679.501, 'duration': 9.77}, {'end': 1711.131, 'text': 'But the final and really most robust solution is to use a slightly more complex recurrent unit that can more effectively track long-term dependencies in the data by controlling what information is passed through and what information is used to update its internal state.', 'start': 1690.95, 'duration': 20.181}], 'summary': 'Optimizing weights and using a complex recurrent unit can prevent rapid shrinking of gradients and enhance tracking of long-term dependencies.', 'duration': 36.493, 'max_score': 1652.778, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU1652778.jpg'}, {'end': 1746.437, 'src': 'embed', 'start': 1720.311, 'weight': 0, 'content': [{'end': 1729.275, 'text': "And today we're going to focus our attention on one type of gated cell called a long short-term memory network, or LSTM,", 'start': 1720.311, 'duration': 8.964}, {'end': 1736.498, 'text': 'which is really well-suited for learning long-term dependencies to overcome this vanishing gradient problem.', 'start': 1729.275, 'duration': 7.223}, {'end': 1746.437, 'text': "And LSTMs work really well on a bunch of different types of tasks, and they're extremely, extremely widely used by the deep learning community.", 'start': 1738.171, 'duration': 8.266}], 'summary': 'Lstms are widely used for learning long-term dependencies due to their effectiveness in overcoming the vanishing gradient problem.', 'duration': 26.126, 'max_score': 1720.311, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU1720311.jpg'}], 'start': 1595.034, 'title': 'Alleviating vanishing gradient problem and understanding lstm networks', 'summary': 'Discusses the vanishing gradient problem in standard rnns and proposes solutions such as choosing suitable activation functions like relu and smart weight initialization. it also explains the concept of long short-term memory (lstm) networks, highlighting their ability to effectively track long-term dependencies in data and their widespread usage in the deep learning community.', 'chapters': [{'end': 1689.271, 'start': 1595.034, 'title': 'Alleviating vanishing gradient problem', 'summary': 'Discusses the vanishing gradient problem in standard rnns, proposing solutions such as choosing suitable activation functions like relu and smart weight initialization to prevent gradients from shrinking, thereby improving information linkage within the network.', 'duration': 94.237, 'highlights': ['Choosing ReLU activation function with a derivative of 1 for x > 0 helps prevent shrinking gradients.', 'Smart weight initialization to the identity matrix prevents rapid shrinking of weights during backpropagation.']}, {'end': 1947.753, 'start': 1690.95, 'title': 'Understanding lstm networks', 'summary': 'Explains the concept of long short-term memory (lstm) networks, highlighting their ability to effectively track long-term dependencies in data and their widespread usage in the deep learning community. it also discusses the structure of lstm cells and the role of gates in selectively controlling the flow of information within the cell.', 'duration': 256.803, 'highlights': ['LSTMs are well-suited for learning long-term dependencies and are widely used in the deep learning community, demonstrating their effectiveness in various tasks.', 'The concept of gated cells, particularly LSTM, allows for the selective control of information flow within the cell, addressing the vanishing gradient problem and enabling the tracking and storage of information throughout many time steps.', 'The structure of LSTM cells includes interacting layers that selectively control the flow of information, with gates consisting of a sigmoid function and pointwise multiplication to enable the addition or removal of information to the cell state.', 'LSTMs process information through four steps, with the sigmoid function in gates determining the retention of information, effectively gating the flow of information within the cell.']}], 'duration': 352.719, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU1595034.jpg', 'highlights': ['LSTMs are widely used in deep learning community, effective in various tasks.', "LSTM's gated cells enable selective control of information flow, addressing vanishing gradient problem.", 'Choosing ReLU activation function with derivative of 1 prevents shrinking gradients.', 'Smart weight initialization to identity matrix prevents rapid shrinking of weights.']}, {'end': 2214.729, 'segs': [{'end': 2023.094, 'src': 'heatmap', 'start': 1960.08, 'weight': 1, 'content': [{'end': 1964.669, 'text': 'Use these two steps together to selectively update their internal state.', 'start': 1960.08, 'duration': 4.589}, {'end': 1967.515, 'text': 'And finally, they generate an output.', 'start': 1965.571, 'duration': 1.944}, {'end': 1970.221, 'text': 'Forget, store, update, output.', 'start': 1968.096, 'duration': 2.125}, {'end': 1974.383, 'text': 'So to walk through this a little bit more.', 'start': 1971.621, 'duration': 2.762}, {'end': 1982.528, 'text': 'the first step in the LSTM is to decide what information is going to be thrown away from the cell state to forget irrelevant history.', 'start': 1974.383, 'duration': 8.145}, {'end': 1990.413, 'text': "And that's a function of both the prior internal state h of t, minus 1, as well as the input x of t,", 'start': 1983.228, 'duration': 7.185}, {'end': 1993.575, 'text': 'because some of that information may not be important.', 'start': 1990.413, 'duration': 3.162}, {'end': 2007.52, 'text': 'Next, the LSTM can decide what part of the new information is relevant and use this to store this information into its cell state.', 'start': 1995.396, 'duration': 12.124}, {'end': 2019.912, 'text': 'Then it takes both the relevant parts of both the prior information as well as the current input and uses this to selectively update its cell state.', 'start': 2009.88, 'duration': 10.032}, {'end': 2023.094, 'text': 'And finally, it can return an output.', 'start': 2021.133, 'duration': 1.961}], 'summary': 'Lstm selectively updates state and generates output.', 'duration': 27.698, 'max_score': 1960.08, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU1960080.jpg'}, {'end': 2077.741, 'src': 'embed', 'start': 2048.417, 'weight': 3, 'content': [{'end': 2058.141, 'text': 'But the intuition and the key takeaway that we want you to have about LSTMs is the sequence of how they regulate information flow and storage.', 'start': 2048.417, 'duration': 9.724}, {'end': 2067.886, 'text': "Forgetting irrelevant history, storing what's new and what's important, using that to update the internal state, and generating an output.", 'start': 2058.902, 'duration': 8.984}, {'end': 2077.741, 'text': 'So this hopefully gives you a bit of sense of how LSTMs can selectively control and regulate the flow of information.', 'start': 2069.795, 'duration': 7.946}], 'summary': "Lstms regulate information flow and storage, forgetting irrelevant history, storing what's new, and generating output.", 'duration': 29.324, 'max_score': 2048.417, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU2048417.jpg'}, {'end': 2132.576, 'src': 'embed', 'start': 2099.062, 'weight': 0, 'content': [{'end': 2107.61, 'text': 'And you can think of this as sort of a highway of cell state, where gradients can flow uninterrupted, as shown here in red.', 'start': 2099.062, 'duration': 8.548}, {'end': 2117.058, 'text': "And this enables us to alleviate and mitigate that vanishing gradient problem that's seen with vanilla or standard RNNs.", 'start': 2108.411, 'duration': 8.647}, {'end': 2118.7, 'text': 'Yeah, question.', 'start': 2118.179, 'duration': 0.521}, {'end': 2132.576, 'text': 'Yeah, so forgetting irrelevant information goes back to the question that was asked a little bit earlier.', 'start': 2125.253, 'duration': 7.323}], 'summary': 'Highway of cell state prevents vanishing gradient problem in rnns.', 'duration': 33.514, 'max_score': 2099.062, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU2099062.jpg'}, {'end': 2181.418, 'src': 'embed', 'start': 2155.105, 'weight': 2, 'content': [{'end': 2167.409, 'text': 'So, over the course of training, you want your LSTM to be able to specifically learn what are those bits of prior history that carry more meaning,', 'start': 2155.105, 'duration': 12.304}, {'end': 2172.871, 'text': 'that are important in trying to actually learn the problem of predicting the next word.', 'start': 2167.409, 'duration': 5.462}, {'end': 2181.418, 'text': 'And you want to discard what is not relevant to really enable more robust training.', 'start': 2173.451, 'duration': 7.967}], 'summary': 'Lstm training aims to learn important historical context for predicting the next word and discard irrelevant information for robust training.', 'duration': 26.313, 'max_score': 2155.105, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU2155105.jpg'}], 'start': 1949.314, 'title': 'Lstm computation and training', 'summary': 'Covers the lstm computation process, including four main steps - forget, store, update, and output gates, and highlights the regulation of information flow, mitigation of vanishing gradient problem, and the importance of training lstm models for robust learning.', 'chapters': [{'end': 2048.016, 'start': 1949.314, 'title': 'Lstm computation process', 'summary': 'Explains the lstm computation process, which involves four main steps - forgetting irrelevant history, storing relevant new information, selectively updating the internal state, and generating an output, with emphasis on the forget, store, update, and output gates.', 'duration': 98.702, 'highlights': ['The LSTM involves four main steps: forgetting irrelevant history, storing relevant new information, selectively updating the internal state, and generating an output, with emphasis on the forget, store, update, and output gates.', 'The forget gate determines what information is discarded from the cell state based on the prior internal state and the input, allowing the LSTM to discard irrelevant history.', 'The LSTM uses the input and the prior information to selectively update its cell state, ensuring that only relevant information is stored.', 'The output gate controls the information encoded in the cell state that is sent to the network as input in the next time step.']}, {'end': 2132.576, 'start': 2048.417, 'title': 'Understanding lstms information flow', 'summary': "Explains how lstms regulate information flow, store what's important, and mitigate the vanishing gradient problem, enabling uninterrupted flow of gradients through time.", 'duration': 84.159, 'highlights': ["LSTMs regulate information flow by forgetting irrelevant history and storing what's new and important, thus updating the internal state and generating an output.", 'The internal cell state C in LSTMs acts as a highway for uninterrupted flow of gradients through time, mitigating the vanishing gradient problem seen in standard RNNs.', 'The key takeaway about LSTMs is their sequence of regulating information flow and storage to alleviate the vanishing gradient problem.']}, {'end': 2214.729, 'start': 2133.096, 'title': 'Lstm training and key concepts', 'summary': 'Discusses the importance of training lstm models to learn and discard irrelevant information, in order to enable more robust training, and aims to distill key concepts and takeaways at the end of each lecture.', 'duration': 81.633, 'highlights': ['The importance of training LSTM models to specifically learn which bits of prior history carry more meaning and discard what is not relevant, in order to enable more robust training.', 'The goal of providing periodic summaries is to distill down all the material into key concepts and takeaways that learners should have at the end of each lecture and ultimately at the end of the course.']}], 'duration': 265.415, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU1949314.jpg', 'highlights': ['The internal cell state C in LSTMs acts as a highway for uninterrupted flow of gradients through time, mitigating the vanishing gradient problem seen in standard RNNs.', 'The LSTM involves four main steps: forgetting irrelevant history, storing relevant new information, selectively updating the internal state, and generating an output, with emphasis on the forget, store, update, and output gates.', 'The importance of training LSTM models to specifically learn which bits of prior history carry more meaning and discard what is not relevant, in order to enable more robust training.', "LSTMs regulate information flow by forgetting irrelevant history and storing what's new and important, thus updating the internal state and generating an output."]}, {'end': 2723.778, 'segs': [{'end': 2247.411, 'src': 'embed', 'start': 2215.749, 'weight': 4, 'content': [{'end': 2218.331, 'text': "And so for LSTMs, let's break it down.", 'start': 2215.749, 'duration': 2.582}, {'end': 2224.494, 'text': 'LSTMs are able to maintain this separate cell state independent of what is outputted.', 'start': 2219.311, 'duration': 5.183}, {'end': 2235.24, 'text': 'And they use gates to control the flow of information by forgetting irrelevant history, storing relevant new information selectively,', 'start': 2225.195, 'duration': 10.045}, {'end': 2240.904, 'text': 'updating their cell state and then outputting a filtered version as the output.', 'start': 2235.24, 'duration': 5.664}, {'end': 2247.411, 'text': 'And the key point in terms of training an LSTM is that maintaining this separate,', 'start': 2241.744, 'duration': 5.667}], 'summary': 'Lstms use gates to control information flow and maintain separate cell state.', 'duration': 31.662, 'max_score': 2215.749, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU2215749.jpg'}, {'end': 2292.506, 'src': 'embed', 'start': 2265.331, 'weight': 7, 'content': [{'end': 2274.117, 'text': "I'd like to consider a few concrete examples of how RNNs can be used and some of the amazing applications they enable.", 'start': 2265.331, 'duration': 8.786}, {'end': 2286.063, 'text': "Imagine that we're trying to learn an RNN that can predict the next musical note in a sequence of music and to actually use this to generate brand new musical sequences.", 'start': 2275.038, 'duration': 11.025}, {'end': 2292.506, 'text': 'And the way you could think about this potentially working is by inputting a sequence of notes,', 'start': 2287.104, 'duration': 5.402}], 'summary': 'Rnns enable applications like predicting musical notes and generating new sequences.', 'duration': 27.175, 'max_score': 2265.331, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU2265331.jpg'}, {'end': 2356.451, 'src': 'embed', 'start': 2316.743, 'weight': 5, 'content': [{'end': 2322.987, 'text': "And so for example, here's a case where an RNN was trained on the music of my favorite composer, Chopin.", 'start': 2316.743, 'duration': 6.244}, {'end': 2330.651, 'text': "And the sample I'll play for you is music that was completely generated by this AI.", 'start': 2323.847, 'duration': 6.804}, {'end': 2351.468, 'text': 'So it gives you a little sample that it sounds pretty realistic.', 'start': 2347.987, 'duration': 3.481}, {'end': 2356.451, 'text': "And you'll actually get some practice doing this in today's lab,", 'start': 2352.509, 'duration': 3.942}], 'summary': "An rnn was trained on chopin's music to generate a realistic ai-generated sample.", 'duration': 39.708, 'max_score': 2316.743, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU2316743.jpg'}, {'end': 2412.247, 'src': 'embed', 'start': 2387.524, 'weight': 3, 'content': [{'end': 2395.148, 'text': 'that takes as input the words in a sentence, outputs the sentiment or the feeling or the emotion of that particular sentence,', 'start': 2387.524, 'duration': 7.624}, {'end': 2397.809, 'text': 'which can be either positive or negative.', 'start': 2395.148, 'duration': 2.661}, {'end': 2405.062, 'text': 'And so, for example, if we train a model like this with a bunch of tweets sourced from Twitter,', 'start': 2399.178, 'duration': 5.884}, {'end': 2412.247, 'text': 'we can train our RNN to predict that this first tweet about our class 6S191, has a positive sentiment,', 'start': 2405.062, 'duration': 7.185}], 'summary': 'Rnn model predicts sentiment of input sentence as positive or negative.', 'duration': 24.723, 'max_score': 2387.524, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU2387524.jpg'}, {'end': 2511.626, 'src': 'embed', 'start': 2481.911, 'weight': 0, 'content': [{'end': 2488.755, 'text': 'because you may imagine having a large body of text that you may want to translate.', 'start': 2481.911, 'duration': 6.844}, {'end': 2497.652, 'text': 'But to get around this problem, the researchers at Google were very clever and developed an extension of RNNs called attention.', 'start': 2489.744, 'duration': 7.908}, {'end': 2505.139, 'text': 'And the idea here is that instead of the decoder only having access to the final encoded state,', 'start': 2498.593, 'duration': 6.546}, {'end': 2511.626, 'text': 'it can now actually access the states of all the time steps in the original sentence.', 'start': 2505.139, 'duration': 6.487}], 'summary': 'Google researchers developed attention, allowing decoder access to all time steps in original sentence.', 'duration': 29.715, 'max_score': 2481.911, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU2481911.jpg'}, {'end': 2587.226, 'src': 'embed', 'start': 2561.457, 'weight': 2, 'content': [{'end': 2566.38, 'text': 'And at any moment in time, an autonomous vehicle, like a self-driving car,', 'start': 2561.457, 'duration': 4.923}, {'end': 2571.964, 'text': 'needs to have an understanding of not only where every object in its environment is,', 'start': 2566.38, 'duration': 5.584}, {'end': 2576.407, 'text': 'but also have a sense of where those objects may move in the future.', 'start': 2571.964, 'duration': 4.443}, {'end': 2581.831, 'text': 'And so this is an example of a self-driving car from the company Waymo from Google.', 'start': 2577.068, 'duration': 4.763}, {'end': 2587.226, 'text': 'that encounters a cyclist on its right side, which is denoted in red.', 'start': 2583.063, 'duration': 4.163}], 'summary': 'Self-driving cars need to track objects and predict their movements, illustrated by a waymo car encountering a cyclist.', 'duration': 25.769, 'max_score': 2561.457, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU2561457.jpg'}, {'end': 2644.238, 'src': 'embed', 'start': 2609.557, 'weight': 1, 'content': [{'end': 2617.84, 'text': 'Another example of how we can use deep sequence modeling is in environmental modeling and climate pattern analysis and prediction.', 'start': 2609.557, 'duration': 8.283}, {'end': 2626.843, 'text': 'And so this is a visualization of the predicted patterns for different environmental markers, ranging from wind speeds to humidity,', 'start': 2617.88, 'duration': 8.963}, {'end': 2629.184, 'text': 'to levels of particulate matter in the air.', 'start': 2626.843, 'duration': 2.341}, {'end': 2644.238, 'text': 'And effectively predicting the future behavior of these markers could really be key in projecting and planning for the climate impact of particular human interventions or actions.', 'start': 2629.964, 'duration': 14.274}], 'summary': 'Deep sequence modeling aids in predicting environmental markers for climate impact planning.', 'duration': 34.681, 'max_score': 2609.557, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU2609557.jpg'}, {'end': 2721.795, 'src': 'embed', 'start': 2663.223, 'weight': 6, 'content': [{'end': 2667.567, 'text': 'by defining this recurrence relation, how we can train RNNs,', 'start': 2663.223, 'duration': 4.344}, {'end': 2673.293, 'text': 'and we also looked at how gated cells like LSTMs can help us model long-term dependencies.', 'start': 2667.567, 'duration': 5.726}, {'end': 2679.239, 'text': 'And finally, we discussed some applications of RNNs, including music generation.', 'start': 2673.934, 'duration': 5.305}, {'end': 2686.767, 'text': 'And so that leads in very nicely to the next portion of today, which is our software lab.', 'start': 2680.444, 'duration': 6.323}, {'end': 2690.829, 'text': "And today's lab is going to be broken down into two parts.", 'start': 2687.387, 'duration': 3.442}, {'end': 2696.772, 'text': "We're first going to have a crash course in TensorFlow that covers all the fundamentals.", 'start': 2691.609, 'duration': 5.163}, {'end': 2703.015, 'text': "And then you'll move into actually training RNNs to perform music generation.", 'start': 2697.352, 'duration': 5.663}, {'end': 2711.424, 'text': 'And so for those of you who will stay for the labs, the instructions are up here.', 'start': 2704.876, 'duration': 6.548}, {'end': 2719.853, 'text': 'And Alexander, myself, and all the TAs will be available to assist and field questions as you work through them.', 'start': 2712.044, 'duration': 7.809}, {'end': 2721.795, 'text': 'So thank you very much.', 'start': 2720.954, 'duration': 0.841}], 'summary': 'The transcript covers training rnns, lstms, music generation, and a tensorflow lab session.', 'duration': 58.572, 'max_score': 2663.223, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU2663223.jpg'}], 'start': 2215.749, 'title': 'Lstm and rnn applications', 'summary': 'Delves into the fundamental workings of lstms and rnns, discussing their applications in music generation and sentiment analysis, and their potential in training models for tasks like generating irish folk songs and analyzing sentiments in tweets. additionally, it highlights the relevance of rnns in real-world applications such as language translation, autonomous vehicles, environmental modeling, and music generation.', 'chapters': [{'end': 2429.248, 'start': 2215.749, 'title': 'Lstm and rnn applications', 'summary': 'Discusses the fundamental workings of lstms and rnns, their applications in music generation and sentiment analysis, and their potential in training models for various tasks, such as generating irish folk songs and analyzing sentiments in tweets.', 'duration': 213.499, 'highlights': ['LSTMs use gates to control the flow of information, allowing for efficient training through back propagation through time.', "RNNs can be trained to predict musical notes and generate new music sequences, as demonstrated by an AI generating music based on Chopin's compositions.", 'An RNN can analyze the sentiment of sentences, such as tweets, to determine if they convey positive or negative emotions.', 'RNNs have diverse applications in industry and are widely used for various tasks.']}, {'end': 2723.778, 'start': 2429.948, 'title': 'Relevance of rnns in real-world applications', 'summary': 'Highlights the significance of recurrent neural networks (rnns) in various real-world applications, including language translation, autonomous vehicles, environmental modeling, and music generation.', 'duration': 293.83, 'highlights': ['RNNs used in language translation with attention mechanism to overcome information bottleneck and effectively capture important information in original sentence.', "Application of RNNs in autonomous vehicles to understand and predict movements of surrounding objects for proactive responses, such as Waymo's self-driving car recognizing a cyclist's potential lane change and adjusting its movement.", 'Utilization of deep sequence modeling in environmental modeling and climate pattern analysis, emphasizing the importance of predicting future environmental markers for climate impact projections and planning.', "Overview of RNNs' capabilities for processing sequential data, including training, modeling long-term dependencies using gated cells like LSTMs, and applications in music generation.", 'Announcement of the upcoming software lab focused on training RNNs for music generation and availability of instructors for assistance and questions.']}], 'duration': 508.029, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SEnXr6v2ifU/pics/SEnXr6v2ifU2215749.jpg', 'highlights': ['RNNs used in language translation with attention mechanism to overcome information bottleneck and effectively capture important information in original sentence.', 'Utilization of deep sequence modeling in environmental modeling and climate pattern analysis, emphasizing the importance of predicting future environmental markers for climate impact projections and planning.', "Application of RNNs in autonomous vehicles to understand and predict movements of surrounding objects for proactive responses, such as Waymo's self-driving car recognizing a cyclist's potential lane change and adjusting its movement.", 'An RNN can analyze the sentiment of sentences, such as tweets, to determine if they convey positive or negative emotions.', 'LSTMs use gates to control the flow of information, allowing for efficient training through back propagation through time.', "RNNs can be trained to predict musical notes and generate new music sequences, as demonstrated by an AI generating music based on Chopin's compositions.", "Overview of RNNs' capabilities for processing sequential data, including training, modeling long-term dependencies using gated cells like LSTMs, and applications in music generation.", 'RNNs have diverse applications in industry and are widely used for various tasks.', 'Announcement of the upcoming software lab focused on training RNNs for music generation and availability of instructors for assistance and questions.']}], 'highlights': ['RNNs used in language translation with attention mechanism to overcome information bottleneck and effectively capture important information in original sentence.', 'Utilization of deep sequence modeling in environmental modeling and climate pattern analysis, emphasizing the importance of predicting future environmental markers for climate impact projections and planning.', "Application of RNNs in autonomous vehicles to understand and predict movements of surrounding objects for proactive responses, such as Waymo's self-driving car recognizing a cyclist's potential lane change and adjusting its movement.", 'An RNN can analyze the sentiment of sentences, such as tweets, to determine if they convey positive or negative emotions.', 'LSTMs use gates to control the flow of information, allowing for efficient training through back propagation through time.', "RNNs can be trained to predict musical notes and generate new music sequences, as demonstrated by an AI generating music based on Chopin's compositions.", "Overview of RNNs' capabilities for processing sequential data, including training, modeling long-term dependencies using gated cells like LSTMs, and applications in music generation.", 'RNNs have diverse applications in industry and are widely used for various tasks.', 'Announcement of the upcoming software lab focused on training RNNs for music generation and availability of instructors for assistance and questions.', 'LSTMs are widely used in deep learning community, effective in various tasks.', "LSTM's gated cells enable selective control of information flow, addressing vanishing gradient problem.", 'The internal cell state C in LSTMs acts as a highway for uninterrupted flow of gradients through time, mitigating the vanishing gradient problem seen in standard RNNs.', 'The LSTM involves four main steps: forgetting irrelevant history, storing relevant new information, selectively updating the internal state, and generating an output, with emphasis on the forget, store, update, and output gates.', 'The importance of training LSTM models to specifically learn which bits of prior history carry more meaning and discard what is not relevant, in order to enable more robust training.', "LSTMs regulate information flow by forgetting irrelevant history and storing what's new and important, thus updating the internal state and generating an output.", 'Three ways to address vanishing gradient issue: choosing activation function, clever weight initialization, and designing efficient network architecture.', 'Exploding gradients problem can occur when weight matrix or gradients are large, leading to optimization issues.', 'Vanishing gradient problem arises when gradients become increasingly smaller, hindering network training.', 'Gradients flow across the chain of repeating modules, involving matrix multiplications and activation function derivatives between each time step.', 'The flow of errors is traced back to the beginning of the data sequence, requiring repeated matrix multiplications and derivative computations.', 'Cell update results from a nonlinear activation function, necessitating repeated multiplications of the weight matrix and the derivative of the activation function.', 'RNNs are well-suited for handling sequential data and maintaining information over time, making them suitable for problems involving sequences of inputs and outputs such as sentiment analysis, text or music generation.', 'Sequence models need to handle variable length input sequences and track long-term dependencies in the data.', 'The need for being able to track long-term dependencies and have parameters that can be shared across the entirety of the sequence is emphasized.', 'Developing models that can handle variable length input sequences and track long-term dependencies in the data is crucial for sequence modeling.', 'RNNs utilize loops to pass information from one time step to the next, allowing information to persist over time, and they apply a simple recurrence relation to process sequential data.', "The RNN algorithm involves initializing the network and hidden state, looping through the input sequence, and updating the hidden state to predict the next word in the sequence, ultimately producing the RNN's output.", 'Back-propagation through time is the algorithm for training RNNs, involving back-propagating errors at each individual time step and across all time steps, enabling adjustments to the parameters to minimize the loss.', 'Examples of applications for sequential processing include analyzing medical signals like EEGs, projecting stock prices, and inferring and understanding genomic sequences.', 'Sequential data is all around us, from audio being split up into a sequence of sound waves to text being split up into sequences of characters or words.', 'The feed-forward network can only take a fixed length input vector, creating a challenge in handling variable length inputs.', "Using a fixed window approach restricts the model's ability to effectively model long-term dependencies, impacting the accuracy of predictions.", 'Representing the sequence as a bag of words results in the loss of sequential information and prior history, leading to inaccurate representations of sentences with different semantic meanings.']}