title
MIT 6.S191 (2021): Recurrent Neural Networks

description
MIT Introduction to Deep Learning 6.S191: Lecture 2 Recurrent Neural Networks Lecturer: Ava Soleimany January 2021 For all lectures, slides, and lab materials: http://introtodeeplearning.com​ Lecture Outline 0:00​ - Introduction 2:37​ - Sequence modeling 4:54​ - Neurons with recurrence 12:07​ - Recurrent neural networks 14:13​ - RNN intuition 17:01​ - Unfolding RNNs 18:39 - RNNs from scratch 22:12 - Design criteria for sequential modelling 23:37 - Word prediction example 31:31​ - Backpropagation through time 33:40​ - Gradient issues 38:46​ - Long short term memory (LSTM) 47:47​ - RNN applications 52:15​ - Attention 59:24​ - 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 (2021): Recurrent Neural Networks', 'heatmap': [{'end': 2037.54, 'start': 1993.063, 'weight': 0.715}, {'end': 2325.073, 'start': 2277.931, 'weight': 1}, {'end': 2724.75, 'start': 2640.336, 'weight': 0.799}], 'summary': 'Covers deep sequence modeling, sequence modeling in machine learning, recurrent neural networks, rnn forward pass and implementation, encoding language data with rnns, rnn challenges, lstms and rnns in tensorflow, and limitations of rnns, highlighting applications like predicting ball position, student performance, sentiment analysis, language transformation, music generation, and autonomous vehicle trajectory prediction.', 'chapters': [{'end': 181.942, 'segs': [{'end': 163.127, 'src': 'embed', 'start': 113.531, 'weight': 0, 'content': [{'end': 118.154, 'text': 'And the truth is that sequential data and these types of problems are really all around us.', 'start': 113.531, 'duration': 4.623}, {'end': 124.38, 'text': 'For example, audio like the waveform from my speech can be split up into a sequence of sound waves.', 'start': 118.695, 'duration': 5.685}, {'end': 131.565, 'text': 'And text can be split up into a sequence of characters or also words,', 'start': 125.439, 'duration': 6.126}, {'end': 139.392, 'text': 'where in here each of these individual characters or each of the individual words can be thought of as a time step in our sequence.', 'start': 131.565, 'duration': 7.827}, {'end': 148.533, 'text': 'Now, beyond these two examples, there are many more cases in which sequential processing can be useful, from medical signals to EKGs,', 'start': 140.646, 'duration': 7.887}, {'end': 153.418, 'text': 'to prediction of stock prices, to genomic or genetic data and beyond.', 'start': 148.533, 'duration': 4.885}, {'end': 157.662, 'text': "So, now that we've gotten a sense of what sequential data looks like,", 'start': 154.399, 'duration': 3.263}, {'end': 163.127, 'text': "let's think about some concrete applications in which sequence modeling plays out in the real world.", 'start': 157.662, 'duration': 5.465}], 'summary': 'Sequential data has various applications, from speech waveforms to medical signals and stock price prediction.', 'duration': 49.596, 'max_score': 113.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE113531.jpg'}], 'start': 10.224, 'title': 'Deep sequence modeling', 'summary': 'Delves into deep sequence modeling, emphasizing the need for distinct network architecture and showcasing applications through examples such as predicting ball position, offering insights into real-world sequential processing applications.', 'chapters': [{'end': 181.942, 'start': 10.224, 'title': 'Deep sequence modeling', 'summary': 'Focuses on deep sequence modeling, explaining the need for a different network architecture, using examples like predicting the next position of a ball and highlighting various applications of sequential processing in real-world scenarios.', 'duration': 171.718, 'highlights': ['The need for a different type of network architecture for sequential processing is explained through examples like predicting the next position of a ball, illustrating the importance of considering previous data to make accurate predictions.', 'Various real-world applications of sequential processing are discussed, including audio waveform analysis, text processing, medical signals, EKGs, stock price prediction, and genomic data.', 'The lecture builds upon the understanding of neural networks and feed-forward models introduced in the first lecture, emphasizing the transition to applying neural networks to problems involving sequential processing of data.']}], 'duration': 171.718, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE10224.jpg', 'highlights': ['Various real-world applications of sequential processing are discussed, including audio waveform analysis, text processing, medical signals, EKGs, stock price prediction, and genomic data.', 'The need for a different type of network architecture for sequential processing is explained through examples like predicting the next position of a ball, illustrating the importance of considering previous data to make accurate predictions.', 'The lecture builds upon the understanding of neural networks and feed-forward models introduced in the first lecture, emphasizing the transition to applying neural networks to problems involving sequential processing of data.']}, {'end': 486.973, 'segs': [{'end': 240.38, 'src': 'embed', 'start': 206.616, 'weight': 4, 'content': [{'end': 208.177, 'text': 'sequential outputs as well.', 'start': 206.616, 'duration': 1.561}, {'end': 216.103, 'text': "So for example, let's consider the case where we have a language processing problem where there's a sentence as input to our model.", 'start': 208.838, 'duration': 7.265}, {'end': 223.248, 'text': 'And that defines a sequence where the words in the sentence are the individual time steps in that sequence.', 'start': 216.923, 'duration': 6.325}, {'end': 231.954, 'text': 'And at the end, our task is to predict one output, which is going to be the sentiment or feeling associated with that sequence input.', 'start': 223.848, 'duration': 8.106}, {'end': 240.38, 'text': 'And you can think of this problem as having a sequence input, single output, or as sort of a many-to-one sequence problem.', 'start': 232.615, 'duration': 7.765}], 'summary': 'Language processing model predicts sentiment from sequence input.', 'duration': 33.764, 'max_score': 206.616, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE206616.jpg'}, {'end': 465.529, 'src': 'embed', 'start': 384.809, 'weight': 0, 'content': [{'end': 394.413, 'text': 'So for example here, we have a single layer of perceptrons in green taking three inputs in blue and predicting four outputs shown in purple.', 'start': 384.809, 'duration': 9.604}, {'end': 398.715, 'text': 'But once again, does this have a notion of time or of sequence??', 'start': 395.233, 'duration': 3.482}, {'end': 407.416, 'text': "No, it doesn't, because again, our inputs and our outputs you can think of as being from a fixed time step in our sequence.", 'start': 399.597, 'duration': 7.819}, {'end': 410.86, 'text': "So let's simplify this diagram.", 'start': 409.42, 'duration': 1.44}, {'end': 419.162, 'text': "And to do that, we'll collapse that hidden layer down to this green box, and our input and output vectors will be as depicted here.", 'start': 411.761, 'duration': 7.401}, {'end': 428.584, 'text': 'And again, our inputs, x, are going to be some vectors of length m, and our outputs are going to be of length n.', 'start': 419.742, 'duration': 8.842}, {'end': 435.085, 'text': "But still we're still considering the input at just a specific time denoted here by t,", 'start': 428.584, 'duration': 6.501}, {'end': 437.746, 'text': 'which is nothing different from what we saw in the first lecture.', 'start': 435.085, 'duration': 2.661}, {'end': 444.267, 'text': 'And even with this simplified representation of a feed forward network,', 'start': 439.099, 'duration': 5.168}, {'end': 452.039, 'text': 'we could naively already try to feed a sequence into this model by just applying that same model over and over again,', 'start': 444.267, 'duration': 7.772}, {'end': 454.022, 'text': 'once for each time step in our sequence.', 'start': 452.039, 'duration': 1.983}, {'end': 462.306, 'text': 'To get a sense of this and how we could handle these individual inputs across different time step,', 'start': 455.319, 'duration': 6.987}, {'end': 465.529, 'text': "let's first just rotate the same diagram from the previous slide.", 'start': 462.306, 'duration': 3.223}], 'summary': 'A simplified representation of a feed forward network taking specific inputs and predicting specific outputs without consideration for time or sequence.', 'duration': 80.72, 'max_score': 384.809, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE384809.jpg'}], 'start': 181.942, 'title': 'Sequence modeling in machine learning', 'summary': 'Discusses the concept of sequence modeling in machine learning, highlighting the extension of possibilities to handle temporal inputs and sequential outputs. it demonstrates examples of predicting student performance and sentiment analysis in language processing. additionally, it introduces the concept of sequence modeling in neural networks, highlighting its applications in one-to-many and many-to-many sequence modeling problems.', 'chapters': [{'end': 246.435, 'start': 181.942, 'title': 'Sequence modeling in machine learning', 'summary': 'Discusses the concept of sequence modeling in machine learning, highlighting the extension of possibilities to handle temporal inputs and sequential outputs, demonstrated through examples of predicting student performance and sentiment analysis in language processing.', 'duration': 64.493, 'highlights': ['Sequence modeling introduces the ability to handle temporal inputs and sequential outputs, enabling the prediction of student performance and sentiment analysis in language processing.', 'In a sequence modeling scenario, a language processing problem can involve a sequence of words as input and predict the sentiment associated with that sequence as output.', 'The concept of sequence modeling expands the range of possibilities in machine learning, allowing for the consideration of temporal inputs and sequential outputs in various applications.']}, {'end': 486.973, 'start': 246.775, 'title': 'Introduction to sequence modeling', 'summary': 'Introduces the concept of sequence modeling in neural networks, highlighting its applications in one-to-many and many-to-many sequence modeling problems, with a focus on understanding the fundamental changes required in neural network architecture to handle sequential data.', 'duration': 240.198, 'highlights': ['The chapter discusses one-to-many and many-to-many sequence modeling problems, with applications in tasks such as sentence captioning and machine translation.', 'The fundamental changes in neural network architecture required to handle sequential data are emphasized, starting from the basics of neural networks and perceptrons.', 'The concept of applying the same model over multiple time steps in a sequence is explored, demonstrating the handling of individual inputs across different time steps.']}], 'duration': 305.031, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE181942.jpg', 'highlights': ['Sequence modeling enables handling temporal inputs and sequential outputs, predicting student performance and sentiment analysis.', 'Sequence modeling expands machine learning possibilities, considering temporal inputs and sequential outputs.', 'Language processing problems involve input sequence of words and predicting associated sentiment as output.', 'Chapter discusses one-to-many and many-to-many sequence modeling problems with applications in sentence captioning and machine translation.', 'Fundamental changes in neural network architecture are emphasized for handling sequential data.', 'Applying the same model over multiple time steps in a sequence is explored, handling individual inputs across different time steps.']}, {'end': 936.673, 'segs': [{'end': 706.121, 'src': 'embed', 'start': 681.232, 'weight': 5, 'content': [{'end': 691.216, 'text': 'And because, as you see, in this relation here our output is now a function of both the current input and the past memory at a previous time step.', 'start': 681.232, 'duration': 9.984}, {'end': 696.357, 'text': 'this means we can describe these neurons via a recurrence relation,', 'start': 691.216, 'duration': 5.141}, {'end': 706.121, 'text': 'which means that we have the cell state that depends on the current input and again on the prior cell states.', 'start': 696.357, 'duration': 9.764}], 'summary': 'Neurons described by recurrence relation, cell state depends on current input and prior states.', 'duration': 24.889, 'max_score': 681.232, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE681232.jpg'}, {'end': 862.984, 'src': 'embed', 'start': 776.963, 'weight': 0, 'content': [{'end': 782.567, 'text': 'Specifically, we define this internal cell state H,', 'start': 776.963, 'duration': 5.604}, {'end': 793.234, 'text': 'And that internal cell state is going to be defined by a function that can be parametrized by a set of weights w,', 'start': 782.567, 'duration': 10.667}, {'end': 798.037, 'text': "which are what we're actually trying to learn over the course of training such a network.", 'start': 793.234, 'duration': 4.803}, {'end': 811.166, 'text': 'And that function f is going to take as input both the input at the current time, step x, as well as the prior state h.', 'start': 799.198, 'duration': 11.968}, {'end': 813.748, 'text': 'And how do we actually find and define this function?', 'start': 811.166, 'duration': 2.582}, {'end': 821.853, 'text': "Again, it's going to be parameterized by a set of weights that are going to be specifically what's learned over the course of training, the model.", 'start': 814.588, 'duration': 7.265}, {'end': 834.041, 'text': 'And a key feature of RNNs is that they use this very same function and this very same set of parameters at every time step of processing the sequence.', 'start': 823.137, 'duration': 10.904}, {'end': 839.683, 'text': 'And of course, the weights are going to change over time, over the course of training.', 'start': 835.481, 'duration': 4.202}, {'end': 841.824, 'text': "And later on, we'll see exactly how.", 'start': 839.963, 'duration': 1.861}, {'end': 850.707, 'text': 'But at each iteration of training, that same set of weights is going to be applied to each of the individual time steps in the sequence.', 'start': 842.504, 'duration': 8.203}, {'end': 862.984, 'text': "All right, so now let's step through the algorithm for updating RNNs to get a better sense of how these networks work.", 'start': 852.639, 'duration': 10.345}], 'summary': 'Rnns use the same function and weights at each time step, learning from input sequences.', 'duration': 86.021, 'max_score': 776.963, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE776963.jpg'}, {'end': 912.573, 'src': 'embed', 'start': 887.921, 'weight': 2, 'content': [{'end': 898.372, 'text': "We're going to loop through the words in this sentence and at each step we're going to feed both the current word and the previous hidden state into our RNN.", 'start': 887.921, 'duration': 10.451}, {'end': 905.16, 'text': 'And this is going to generate a prediction for the next word, as well as an update to the hidden state itself.', 'start': 899.173, 'duration': 5.987}, {'end': 912.573, 'text': "And finally, when we're done processing these four words in the sentence,", 'start': 906.365, 'duration': 6.208}], 'summary': 'Rnn loops through words, generating predictions and updating hidden state.', 'duration': 24.652, 'max_score': 887.921, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE887921.jpg'}], 'start': 487.613, 'title': 'Recurrent neural networks', 'summary': 'Emphasizes capturing sequential data with internal memory in rnns, enabling output dependency on past input, forming a recurrence relation. it also explains internal state maintenance, update via recurrence relation, and the use of specific weights for learning.', 'chapters': [{'end': 748.503, 'start': 487.613, 'title': 'Understanding recurrent neural networks', 'summary': "Emphasizes the need to capture the sequential nature of data by introducing internal memory or cell state in recurrent neural networks, which allows the network's output to depend not only on the current input but also on the past memory, forming a recurrence relation.", 'duration': 260.89, 'highlights': ['Recurrent Neural Networks (RNNs) utilize internal memory or cell state to capture the sequential nature of data.', "The network's output depends on both the current inputs and the past memory of cell state.", 'RNNs are defined by a recurrence relation, where the cell state depends on the current input and prior cell states.']}, {'end': 936.673, 'start': 750.319, 'title': 'Understanding recurrent neural networks', 'summary': 'Explains the concept of recurrent neural networks (rnns), highlighting the maintenance of internal state h, its update via recurrence relation, and the use of a specific set of weights w for learning.', 'duration': 186.354, 'highlights': ['RNNs maintain an internal state H, updated at each time step, through a recurrence relation.', 'The internal cell state H is defined by a function parametrized by weights w, which are learned during the training of the network.', 'RNNs use the same function and set of parameters at every time step of processing the sequence.', 'The RNN algorithm involves looping through words, feeding the current word and previous hidden state into the RNN to generate predictions and update the hidden state.', 'The RNN computation includes internal cell state update to H and the output prediction itself.']}], 'duration': 449.06, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE487613.jpg', 'highlights': ['RNNs utilize internal memory to capture sequential data', "The network's output depends on current inputs and past memory", 'RNNs are defined by a recurrence relation', 'RNNs maintain an internal state H updated at each time step', 'The internal cell state H is defined by a function parametrized by weights', 'RNNs use the same function and set of parameters at every time step', 'RNN algorithm involves looping through words and updating hidden state', 'RNN computation includes internal cell state update and output prediction']}, {'end': 1590.411, 'segs': [{'end': 1244.481, 'src': 'embed', 'start': 1218.085, 'weight': 0, 'content': [{'end': 1226.609, 'text': 'But conveniently, TensorFlow has already implemented these types of RNN cells for us, which you can use via the simple RNN layer.', 'start': 1218.085, 'duration': 8.524}, {'end': 1232.732, 'text': "And you're going to get some practice doing exactly this and using the RNNs later on in today's lab.", 'start': 1227.289, 'duration': 5.443}, {'end': 1235.893, 'text': 'All right.', 'start': 1234.892, 'duration': 1.001}, {'end': 1244.481, 'text': "so, to recap, now that we're at this point in this lecture where we've built up our understanding of RNNs and their mathematical basis,", 'start': 1235.893, 'duration': 8.588}], 'summary': "Tensorflow provides rnn cells, to be used in today's lab.", 'duration': 26.396, 'max_score': 1218.085, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE1218085.jpg'}, {'end': 1442.719, 'src': 'embed', 'start': 1414.4, 'weight': 3, 'content': [{'end': 1424.072, 'text': "so to understand these criteria concretely, I'd like to consider a very concrete sequence modeling problem, which is going to be the following", 'start': 1414.4, 'duration': 9.672}, {'end': 1434.196, 'text': 'Given some series of words in a sentence, our task is going to be to predict the most likely next word to occur in that sentence.', 'start': 1424.732, 'duration': 9.464}, {'end': 1435.976, 'text': 'All right.', 'start': 1435.716, 'duration': 0.26}, {'end': 1439.858, 'text': "So let's suppose we have this sentence as an example.", 'start': 1436.957, 'duration': 2.901}, {'end': 1442.719, 'text': 'This morning, I took my cat for a walk.', 'start': 1440.418, 'duration': 2.301}], 'summary': 'Given a sequence modeling problem, predict next word in a sentence.', 'duration': 28.319, 'max_score': 1414.4, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE1414400.jpg'}, {'end': 1567.441, 'src': 'embed', 'start': 1512.645, 'weight': 1, 'content': [{'end': 1519.131, 'text': 'Instead, neural networks require numerical inputs that can be a vector or an array of numbers,', 'start': 1512.645, 'duration': 6.486}, {'end': 1524.976, 'text': 'such that the model can operate on them to generate a vector or array of numbers as the output.', 'start': 1519.131, 'duration': 5.845}, {'end': 1529.74, 'text': 'So this is going to work for us, but operating just on words simply is not.', 'start': 1525.656, 'duration': 4.084}, {'end': 1540.778, 'text': 'All right, so now we know that we need to have a way to transform language into this vector or array-based representation.', 'start': 1532.312, 'duration': 8.466}, {'end': 1543.42, 'text': 'How exactly are we going to go about this?', 'start': 1541.679, 'duration': 1.741}, {'end': 1548.364, 'text': "The solution we're going to consider is this concept of embedding,", 'start': 1544.441, 'duration': 3.923}, {'end': 1555.69, 'text': 'which is this idea of transforming a set of identifiers for objects effectively indices,', 'start': 1548.364, 'duration': 7.326}, {'end': 1561.074, 'text': 'into a vector of fixed size that captures the content of the input.', 'start': 1555.69, 'duration': 5.384}, {'end': 1567.441, 'text': 'So to think through how we could actually go about doing this for language data.', 'start': 1562.199, 'duration': 5.242}], 'summary': 'Neural networks require numerical inputs for language representation through embedding.', 'duration': 54.796, 'max_score': 1512.645, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE1512645.jpg'}], 'start': 937.354, 'title': 'Rnn forward pass and implementation', 'summary': 'Explains the forward pass through a recurrent neural network, covering the mathematical foundation, updating hidden states, generating output, and determining total loss using tensorflow. it also emphasizes the suitability of rnns for handling sequential data and their applications, design criteria, and language transformation for neural network processing.', 'chapters': [{'end': 1296.157, 'start': 937.354, 'title': 'Rnn forward pass and implementation', 'summary': 'Explains the mathematical foundation for making a forward pass through a recurrent neural network (rnn), highlighting the process of updating the hidden state, generating output, and determining the total loss for training using tensorflow, and emphasizes the suitability of rnns for handling sequential data.', 'duration': 358.803, 'highlights': ['RNNs can handle sequential data by updating the hidden state according to a specific equation, computing the output, and determining the total loss for training.', 'The process of updating the hidden state and generating output in an RNN is described with an example of implementing an RNN from scratch using TensorFlow.', 'The use of RNN cells via the simple RNN layer in TensorFlow is highlighted as a convenient implementation for RNNs.']}, {'end': 1590.411, 'start': 1296.838, 'title': 'Recurrent neural networks', 'summary': 'Covers the applications and design criteria of recurrent neural networks, emphasizing the need to handle variable length sequences and track long-term dependencies, while also discussing the transformation of language into vector representations for neural network processing.', 'duration': 293.573, 'highlights': ['Recurrent neural networks are used in applications like machine translation and music generation, and can handle variable length sequences and track long-term dependencies.', 'The design criteria for sequence modeling include the need to handle variable length sequences and track long-term dependencies, achievable through weight sharing in the network.', 'Language data needs to be transformed into a vector or array-based representation, which is accomplished through the concept of embedding.']}], 'duration': 653.057, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE937354.jpg', 'highlights': ['RNNs can handle sequential data by updating the hidden state according to a specific equation, computing the output, and determining the total loss for training.', 'Recurrent neural networks are used in applications like machine translation and music generation, and can handle variable length sequences and track long-term dependencies.', 'The process of updating the hidden state and generating output in an RNN is described with an example of implementing an RNN from scratch using TensorFlow.', 'The use of RNN cells via the simple RNN layer in TensorFlow is highlighted as a convenient implementation for RNNs.', 'The design criteria for sequence modeling include the need to handle variable length sequences and track long-term dependencies, achievable through weight sharing in the network.', 'Language data needs to be transformed into a vector or array-based representation, which is accomplished through the concept of embedding.']}, {'end': 2037.54, 'segs': [{'end': 2037.54, 'src': 'heatmap', 'start': 1944.331, 'weight': 0, 'content': [{'end': 1953.296, 'text': 'And we take the derivative of the loss with respect to each weight parameter in our network and then adjust the parameters, the weights in our model,', 'start': 1944.331, 'duration': 8.965}, {'end': 1954.817, 'text': 'in order to minimize that loss.', 'start': 1953.296, 'duration': 1.521}, {'end': 1960.601, 'text': 'For RNNs as we walked through earlier.', 'start': 1958.1, 'duration': 2.501}, {'end': 1972.385, 'text': 'our forward pass through the network consists of going forward across time and updating the cell state based on the input as well as the previous state,', 'start': 1960.601, 'duration': 11.784}, {'end': 1977.886, 'text': 'generating an output and, fundamentally, computing the loss values at the individual time,', 'start': 1972.385, 'duration': 5.501}, {'end': 1982.868, 'text': 'steps in our sequence and finally summing those individual losses to get the total loss.', 'start': 1977.886, 'duration': 4.982}, {'end': 1993.063, 'text': 'Instead of back-propagating errors through a single feed-forward network at a single time step in RNNs,', 'start': 1984.898, 'duration': 8.165}, {'end': 2005.37, 'text': 'those errors are going to be back-propagated from the overall loss through each individual time step and then across the time steps all the way from where we are currently in the sequence back to the beginning.', 'start': 1993.063, 'duration': 12.307}, {'end': 2009.473, 'text': "And this is the reason why it's called back-propagation through time.", 'start': 2006.131, 'duration': 3.342}, {'end': 2018.991, 'text': 'because as you can see, all of the errors are going to be flowing back in time from the most recent time step to the very beginning of the sequence.', 'start': 2010.333, 'duration': 8.658}, {'end': 2031.915, 'text': 'Now, if we expand this out and take a closer look at how gradients can actually flow across this chain of repeating recurrent neural network module,', 'start': 2020.563, 'duration': 11.352}, {'end': 2037.54, 'text': 'we can see that between each time step we have to perform this matrix multiplication.', 'start': 2031.915, 'duration': 5.625}], 'summary': 'In rnns, back-propagation through time involves error flow from recent time step to sequence beginning, adjusting weights to minimize loss.', 'duration': 93.209, 'max_score': 1944.331, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE1944331.jpg'}], 'start': 1591.598, 'title': 'Encoding language data with rnns', 'summary': 'Discusses encoding language data using one-hot encoding and learning-based methods. it emphasizes design criteria for recurrent neural network models, including handling variable sequence lengths, capturing long-term dependencies, and modeling differences in sequence order.', 'chapters': [{'end': 2037.54, 'start': 1591.598, 'title': 'Encoding language data with rnns', 'summary': 'Discusses encoding language data using one-hot encoding and learning-based methods, and then delves into the design criteria for recurrent neural network models, emphasizing the ability to handle variable sequence lengths, capture long-term dependencies, and model differences in sequence order.', 'duration': 445.942, 'highlights': ['Recurrent neural networks (RNNs) can handle variable sequence lengths, meeting the first design criterion for sequence modeling.', 'RNNs effectively capture and model long-term dependencies in data, fulfilling the second design criterion for sequence modeling.', 'RNNs can capture differences in sequence order, addressing the need to capture differences in the overall meaning or property of a sequence.', 'Backpropagation through time is employed for training RNNs, involving back-propagating errors from the overall loss through each individual time step and across time steps, from the most recent to the beginning of the sequence.']}], 'duration': 445.942, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE1591598.jpg', 'highlights': ['RNNs can handle variable sequence lengths, meeting the first design criterion for sequence modeling.', 'RNNs effectively capture and model long-term dependencies in data, fulfilling the second design criterion for sequence modeling.', 'RNNs can capture differences in sequence order, addressing the need to capture differences in the overall meaning or property of a sequence.', 'Backpropagation through time is employed for training RNNs, involving back-propagating errors from the overall loss through each individual time step and across time steps, from the most recent to the beginning of the sequence.']}, {'end': 2541.839, 'segs': [{'end': 2272.569, 'src': 'embed', 'start': 2247.322, 'weight': 1, 'content': [{'end': 2254.686, 'text': 'And as this gap grows, standard RNNs become increasingly unable to connect the relevant information.', 'start': 2247.322, 'duration': 7.364}, {'end': 2257.927, 'text': "And that's because of this vanishing grading problem.", 'start': 2255.106, 'duration': 2.821}, {'end': 2265.471, 'text': 'So it relates back to this need to be able to effectively model and capture long-term dependencies in data.', 'start': 2258.788, 'duration': 6.683}, {'end': 2272.569, 'text': "How can we get around this? The first trick we're going to consider is pretty simple.", 'start': 2267.287, 'duration': 5.282}], 'summary': 'Standard rnns struggle with long-term dependencies due to vanishing gradient problem.', 'duration': 25.247, 'max_score': 2247.322, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE2247322.jpg'}, {'end': 2325.073, 'src': 'heatmap', 'start': 2277.931, 'weight': 1, 'content': [{'end': 2288.336, 'text': 'Specifically, what is commonly done is to use a ReLU activation function where the derivative of this activation function is greater than 1,', 'start': 2277.931, 'duration': 10.405}, {'end': 2291.937, 'text': 'for all instances in which x is greater than 0..', 'start': 2288.336, 'duration': 3.601}, {'end': 2304.077, 'text': 'And this helps the value of the gradient with respect to our loss function to actually shrink when the values of its input are greater than zero.', 'start': 2291.937, 'duration': 12.14}, {'end': 2311.681, 'text': 'Another thing we can do is to be smart in how we actually initialize the parameters in our network.', 'start': 2305.956, 'duration': 5.725}, {'end': 2325.073, 'text': 'And we can specifically initialize the weights to the identity matrix to be able to try to prevent them from shrinking to zero completely and very rapidly during backpropagation.', 'start': 2312.642, 'duration': 12.431}], 'summary': 'Using relu activation and weight initialization to prevent gradient shrinking and rapid parameter shrinking during backpropagation.', 'duration': 47.142, 'max_score': 2277.931, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE2277931.jpg'}, {'end': 2311.681, 'src': 'embed', 'start': 2291.937, 'weight': 2, 'content': [{'end': 2304.077, 'text': 'And this helps the value of the gradient with respect to our loss function to actually shrink when the values of its input are greater than zero.', 'start': 2291.937, 'duration': 12.14}, {'end': 2311.681, 'text': 'Another thing we can do is to be smart in how we actually initialize the parameters in our network.', 'start': 2305.956, 'duration': 5.725}], 'summary': 'Optimizing gradient for values > 0, smart parameter initialization.', 'duration': 19.744, 'max_score': 2291.937, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE2291937.jpg'}, {'end': 2394.213, 'src': 'embed', 'start': 2355.57, 'weight': 3, 'content': [{'end': 2359.273, 'text': "Specifically, we're going to use what is called gated cells.", 'start': 2355.57, 'duration': 3.703}, {'end': 2364.138, 'text': "And today we're going to focus on one particular type of gated cell,", 'start': 2360.294, 'duration': 3.844}, {'end': 2369.323, 'text': 'which is definitely the most common and most broadly used in recurrent neural networks.', 'start': 2364.138, 'duration': 5.185}, {'end': 2374.348, 'text': "And that's called the Long Short Term Memory Unit, or LSTM.", 'start': 2369.724, 'duration': 4.624}, {'end': 2394.213, 'text': "And what's cool about LSTMs is that networks that are built using LSTMs are particularly well-suited at better maintaining long-term dependencies in the data and tracking information across multiple time steps to try to overcome this vanishing gradient problem and,", 'start': 2375.109, 'duration': 19.104}], 'summary': 'Lstm, a common gated cell, excels in maintaining long-term dependencies in recurrent neural networks.', 'duration': 38.643, 'max_score': 2355.57, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE2355570.jpg'}, {'end': 2459.062, 'src': 'embed', 'start': 2429.568, 'weight': 0, 'content': [{'end': 2435.433, 'text': 'But hopefully, I hope to provide you with the intuitive understanding about how these networks work.', 'start': 2429.568, 'duration': 5.865}, {'end': 2446.181, 'text': "Alright, so to understand the key operations that make LSTM special, let's first go back to the general structure of an RNN.", 'start': 2436.834, 'duration': 9.347}, {'end': 2453.107, 'text': "And here I'm depicting it slightly differently, but the concept is exactly that from what I introduced previously.", 'start': 2446.862, 'duration': 6.245}, {'end': 2459.062, 'text': 'where we build up our recurrent neural network via this repeating module linked across time.', 'start': 2453.84, 'duration': 5.222}], 'summary': 'Exploring lstm networks for intuitive understanding of rnn operations.', 'duration': 29.494, 'max_score': 2429.568, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE2429568.jpg'}], 'start': 2037.54, 'title': 'Rnn challenges', 'summary': 'Discusses challenges in recurrent neural networks, such as vanishing gradient problem and capturing long-term dependencies. it covers causes, implications, and solutions including gradient clipping, activation function, weight initialization, and introduction of lstm units.', 'chapters': [{'end': 2222.468, 'start': 2037.54, 'title': 'Addressing vanishing gradient problem', 'summary': 'Discusses the vanishing gradient problem in recurrent neural networks, its causes, implications, and solutions including gradient clipping, choice of activation function, weight initialization, and network architecture changes.', 'duration': 184.928, 'highlights': ['The exploding gradient problem can occur when gradient values or weight values are greater than 1, leading to extremely large gradients that hinder network optimization.', 'The vanishing gradient problem can occur when gradients become increasingly smaller, hindering effective training of the network and biasing it towards capturing shorter term dependencies.', 'Gradient clipping is a solution to the exploding gradient problem, which involves scaling back particularly large gradients to mitigate the issue.', 'Addressing the vanishing gradient problem can be achieved by cleverly choosing activation functions, smartly initializing weight matrices, and making changes to the network architecture.', 'The vanishing gradient problem can bias the network towards capturing shorter term dependencies in the data rather than longer term dependencies, impacting tasks like language model training.']}, {'end': 2541.839, 'start': 2223.448, 'title': 'Challenges in recurrent neural networks', 'summary': 'Discusses challenges in capturing long-term dependencies in data using standard rnns, solutions involving activation function selection, weight initialization, and the introduction of long short term memory units (lstm) to effectively model and maintain long-term dependencies in sequential data.', 'duration': 318.391, 'highlights': ['The introduction of Long Short Term Memory Units (LSTM) to effectively model and maintain long-term dependencies in sequential data.', 'Solutions involving activation function selection and weight initialization to address the vanishing gradient problem.', 'Challenges in capturing long-term dependencies in data using standard RNNs.']}], 'duration': 504.299, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE2037540.jpg', 'highlights': ['The introduction of Long Short Term Memory Units (LSTM) to effectively model and maintain long-term dependencies in sequential data.', 'Gradient clipping is a solution to the exploding gradient problem, which involves scaling back particularly large gradients to mitigate the issue.', 'Addressing the vanishing gradient problem can be achieved by cleverly choosing activation functions, smartly initializing weight matrices, and making changes to the network architecture.', 'The vanishing gradient problem can bias the network towards capturing shorter term dependencies in the data rather than longer term dependencies, impacting tasks like language model training.', 'The exploding gradient problem can occur when gradient values or weight values are greater than 1, leading to extremely large gradients that hinder network optimization.']}, {'end': 3248.414, 'segs': [{'end': 2724.75, 'src': 'heatmap', 'start': 2640.336, 'weight': 0.799, 'content': [{'end': 2652.85, 'text': 'LSTMs use this type of operation to process information by first forgetting irrelevant history.', 'start': 2640.336, 'duration': 12.514}, {'end': 2657.752, 'text': 'secondly, by storing the most relevant new information.', 'start': 2652.85, 'duration': 4.902}, {'end': 2662.015, 'text': 'thirdly, by updating their internal cell state and then generating a output.', 'start': 2657.752, 'duration': 4.263}, {'end': 2667.238, 'text': 'The first step is to forget irrelevant parts of the previous state.', 'start': 2662.975, 'duration': 4.263}, {'end': 2675.502, 'text': 'And this is achieved by taking the previous state and passing it through one of these sigmoid gates, which, again,', 'start': 2667.858, 'duration': 7.644}, {'end': 2680.365, 'text': 'you can think of it as modulating how much should be passed in or kept out?', 'start': 2675.502, 'duration': 4.863}, {'end': 2692.422, 'text': 'The next step is to determine what part of the new information and what part of the old information is relevant and to store this into the cell state.', 'start': 2681.789, 'duration': 10.633}, {'end': 2702.635, 'text': 'And what is really critical about LSTMs is that they maintain the separate value of the cell state C.', 'start': 2694.853, 'duration': 7.782}, {'end': 2705.216, 'text': 'in addition to what we introduced previously.', 'start': 2702.635, 'duration': 2.581}, {'end': 2714.239, 'text': 'H And C is what is going to be selectively updated via these gatewise operations.', 'start': 2705.216, 'duration': 9.023}, {'end': 2720.146, 'text': 'Finally, we can return an output from our LSTM.', 'start': 2716.363, 'duration': 3.783}, {'end': 2724.75, 'text': 'And so there is a interacting layer, an output gate,', 'start': 2720.547, 'duration': 4.203}], 'summary': 'Lstms process information by forgetting irrelevant history, storing relevant new information, updating the internal cell state, and generating an output.', 'duration': 84.414, 'max_score': 2640.336, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE2640336.jpg'}, {'end': 2798.498, 'src': 'embed', 'start': 2771.892, 'weight': 1, 'content': [{'end': 2784.735, 'text': 'And the key way that they help during training is that all of these different gating mechanisms actually work to allow for what I like to call the uninterrupted flow of gradient computation over time.', 'start': 2771.892, 'duration': 12.843}, {'end': 2792.076, 'text': 'And this is done by maintaining this separate cell state c,', 'start': 2785.695, 'duration': 6.381}, {'end': 2798.498, 'text': 'across which the actual gradient computation so taking the derivative with respect to the weights,', 'start': 2792.076, 'duration': 6.422}], 'summary': 'Gating mechanisms enable uninterrupted flow of gradient computation during training.', 'duration': 26.606, 'max_score': 2771.892, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE2771892.jpg'}, {'end': 3121.482, 'src': 'embed', 'start': 3048.774, 'weight': 0, 'content': [{'end': 3057.981, 'text': "So pretty awesome, right? And I hope you agree that I think it's really exciting to see neural networks being put to the test here.", 'start': 3048.774, 'duration': 9.207}, {'end': 3067.749, 'text': 'But also, at least for me, this sparks some questioning and understanding about what is the line between artificial intelligence and human creativity.', 'start': 3058.322, 'duration': 9.427}, {'end': 3070.672, 'text': "And you'll get a chance to explore this in today's lab.", 'start': 3068.21, 'duration': 2.462}, {'end': 3081.352, 'text': 'Another cool example is, beyond music generation, is one in language processing where we can go from an input sequence, like a sentence,', 'start': 3072.17, 'duration': 9.182}, {'end': 3089.318, 'text': "to a single output, where we can train an RNN to take this to train an RNN to, let's say,", 'start': 3081.352, 'duration': 7.966}, {'end': 3096.904, 'text': 'produce a prediction of emotion or sentiment associated with a particular sentence, either positive or negative.', 'start': 3089.318, 'duration': 7.586}, {'end': 3103.729, 'text': 'And this is effectively a classification task, much like what we saw in the first lecture, except again,', 'start': 3097.504, 'duration': 6.225}, {'end': 3107.712, 'text': "we're operating over a sequence where we have this time component.", 'start': 3103.729, 'duration': 3.983}, {'end': 3113.977, 'text': 'So because this is a classification problem, we can train these networks using a cross-entropy loss.', 'start': 3108.653, 'duration': 5.324}, {'end': 3121.482, 'text': 'And one application that we may be interested in is classifying the sentiments associated with tweets.', 'start': 3114.837, 'duration': 6.645}], 'summary': 'Exploring the application of neural networks in music generation and language processing, including sentiment analysis of tweets.', 'duration': 72.708, 'max_score': 3048.774, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE3048774.jpg'}, {'end': 3178.408, 'src': 'embed', 'start': 3146.698, 'weight': 2, 'content': [{'end': 3149.919, 'text': "And that's this idea of machine translation,", 'start': 3146.698, 'duration': 3.221}, {'end': 3156.223, 'text': 'where our goal is to input a sentence in one language and train an RNN to output a sentence in another language.', 'start': 3149.919, 'duration': 6.304}, {'end': 3171.776, 'text': 'And this can be done by having an encoder component which effectively encodes the original sentence into some state vector and a decoder component which decodes that state vector into the target language,', 'start': 3157.355, 'duration': 14.421}, {'end': 3172.457, 'text': 'the new language.', 'start': 3171.776, 'duration': 0.681}, {'end': 3178.408, 'text': "But it's quite remarkable that,", 'start': 3174.127, 'duration': 4.281}], 'summary': 'Goal: train rnn to translate sentences between languages.', 'duration': 31.71, 'max_score': 3146.698, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE3146698.jpg'}], 'start': 2541.839, 'title': 'Lstms and rnns in tensorflow', 'summary': "Delves into lstm cells in tensorflow, their control of information flow, and rnns' applications, emphasizing practical uses in music generation, sentiment analysis, and machine translation, while addressing issues like the vanishing gradient problem and encoding bottleneck.", 'chapters': [{'end': 2582.351, 'start': 2541.839, 'title': 'Lstm cell in tensorflow', 'summary': 'Explains how lstm cells in tensorflow use different interacting layers to effectively control the flow of information, enabling them to track and store information throughout many time steps.', 'duration': 40.512, 'highlights': ['The LSTM cells use different interacting layers to control the flow of information, enabling them to track and store information throughout many time steps.', 'The LSTM layer can be defined using TensorFlow.', 'The repeating recurrent unit contains interacting layers defined by standard neural network operations like sigmoid and tanh, which can effectively control the flow of information through the LSTM cell.']}, {'end': 2947.43, 'start': 2584.226, 'title': 'Understanding lstms and their applications', 'summary': 'Explains the working of lstms, emphasizing their ability to regulate information flow, maintain a separate cell state, control the flow of information, and mitigate the vanishing gradient problem in rnns. it also highlights the practical application of rnns in music generation.', 'duration': 363.204, 'highlights': ['LSTMs use gates to control the flow of information by forgetting irrelevant information, storing relevant information, updating their cell state, and outputting predictions at each time step.', 'LSTMs maintain a separate cell state CFT, allowing for uninterrupted gradient flow and more efficient training, making them commonly used in modern deep learning.', 'Recurrent neural networks can be deployed for music generation and prediction by training models to predict the next musical note and generate new musical sequences.']}, {'end': 3248.414, 'start': 2948.47, 'title': 'Recurrent neural networks in applications', 'summary': "Discusses the application of recurrent neural networks (rnns) in various domains, such as music composition, sentiment analysis, and machine translation, highlighting the use of rnns in completing franz schubert's unfinished symphony, predicting sentiment in tweets, and enabling machine translation, while addressing potential issues with rnns, including encoding bottleneck and efficiency concerns.", 'duration': 299.944, 'highlights': ["Using RNNs to complete Franz Schubert's unfinished symphony", 'Training RNNs for sentiment analysis and tweet classification', 'Application of RNNs in machine translation, with encoding bottleneck and efficiency concerns']}], 'duration': 706.575, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE2541839.jpg', 'highlights': ['LSTMs use gates to control information flow, forgetting irrelevant data, and updating cell state.', 'LSTM cells track and store information across many time steps using interacting layers.', 'RNNs can be used for music generation and prediction by training models to predict the next musical note.', 'RNNs are applied in sentiment analysis and tweet classification for efficient training.', 'RNNs are used in machine translation, addressing encoding bottleneck and efficiency concerns.', 'LSTM layer can be defined using TensorFlow, employing standard neural network operations.']}, {'end': 3628.545, 'segs': [{'end': 3514.689, 'src': 'embed', 'start': 3489.242, 'weight': 0, 'content': [{'end': 3495.505, 'text': 'And as you can see, the cyclist is approaching a stopped vehicle.', 'start': 3489.242, 'duration': 6.263}, {'end': 3500.441, 'text': 'shown here in purple.', 'start': 3499.54, 'duration': 0.901}, {'end': 3509.726, 'text': 'And the car, the self-driving car, can be able to recognize that the cyclist is now going to merge in front of the car.', 'start': 3501.061, 'duration': 8.665}, {'end': 3514.689, 'text': 'And before it does so, the self-driving car pulls back and stops.', 'start': 3510.346, 'duration': 4.343}], 'summary': 'Self-driving car detects cyclist, pulls back and stops.', 'duration': 25.447, 'max_score': 3489.242, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE3489242.jpg'}, {'end': 3593.012, 'src': 'embed', 'start': 3566.848, 'weight': 1, 'content': [{'end': 3575.497, 'text': "so hopefully over the course of this lecture you've gotten a sense of how recurrent neural networks work and why they're so powerful for processing sequential data.", 'start': 3566.848, 'duration': 8.649}, {'end': 3585.328, 'text': 'We saw how we can model sequences via this defined recurrence relation, and how we could train them using the back propagation through time algorithm.', 'start': 3576.238, 'duration': 9.09}, {'end': 3593.012, 'text': 'We then explored a bit about how gated cells like LSTMs could help us model long-term dependencies in data,', 'start': 3586.189, 'duration': 6.823}], 'summary': 'Recurrent neural networks are powerful for processing sequential data, with lstm cells modeling long-term dependencies.', 'duration': 26.164, 'max_score': 3566.848, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE3566848.jpg'}, {'end': 3628.545, 'src': 'embed', 'start': 3611.536, 'weight': 3, 'content': [{'end': 3619.58, 'text': 'And we encourage you to come to the class and the lab office, our GatherTown session, to discuss the labs.', 'start': 3611.536, 'duration': 8.044}, {'end': 3624.423, 'text': 'ask your questions about both lab content as well as content from the lectures.', 'start': 3619.58, 'duration': 4.843}, {'end': 3627.664, 'text': 'And we look forward to seeing you there.', 'start': 3625.143, 'duration': 2.521}, {'end': 3628.545, 'text': 'Thank you.', 'start': 3628.245, 'duration': 0.3}], 'summary': 'Encouraging students to attend gathertown sessions to discuss labs and lectures.', 'duration': 17.009, 'max_score': 3611.536, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE3611536.jpg'}], 'start': 3249.617, 'title': 'Limitations of recurrent neural networks', 'summary': 'Discusses the inefficiencies of traditional recurrent neural networks in handling long temporal dependencies, backpropagation through time, and memory capacity. it introduces attention as a solution and its application in autonomous vehicles for trajectory prediction and environmental modeling for climate pattern analysis and prediction.', 'chapters': [{'end': 3628.545, 'start': 3249.617, 'title': 'Limitations of recurrent neural networks', 'summary': 'Discusses the limitations of traditional recurrent neural networks, such as inefficiency in handling long temporal dependencies, expensive backpropagation through time, and limited memory capacity, and introduces the concept of attention as a solution, which is the basis of powerful transformer models. it also highlights the application of attention in autonomous vehicles for trajectory prediction and environmental modeling for climate pattern analysis and prediction.', 'duration': 378.928, 'highlights': ['The concept of attention was introduced to overcome the limitations of traditional recurrent neural networks, allowing the network to efficiently capture long-term dependencies as well as short-term dependencies, and it serves as the basis for powerful transformer models.', 'The application of attention is crucial in autonomous vehicles for trajectory prediction, enabling effective predictions about the movement of dynamic objects in the scene, and in environmental modeling for climate pattern analysis and prediction.', 'Traditional recurrent neural networks suffer from limitations such as inefficiency in handling long temporal dependencies, expensive backpropagation through time, and limited memory capacity, which led to the development of attention-based solutions.', 'The lecturer discussed the transition to the lab sessions for implementing recurrent neural networks using TensorFlow and encouraged students to participate in discussions to address questions and gain a better understanding of the content from the lectures.']}], 'duration': 378.928, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/qjrad0V0uJE/pics/qjrad0V0uJE3249617.jpg', 'highlights': ['The concept of attention overcomes limitations of traditional recurrent neural networks, capturing long-term dependencies and serving as the basis for powerful transformer models.', 'The application of attention is crucial in autonomous vehicles for trajectory prediction and environmental modeling for climate pattern analysis and prediction.', 'Traditional recurrent neural networks suffer from limitations in handling long temporal dependencies, backpropagation through time, and memory capacity, leading to the development of attention-based solutions.', 'The lecturer discussed the transition to lab sessions for implementing recurrent neural networks using TensorFlow and encouraged student participation.']}], 'highlights': ['Various real-world applications of sequential processing are discussed, including audio waveform analysis, text processing, medical signals, EKGs, stock price prediction, and genomic data.', 'Sequence modeling enables handling temporal inputs and sequential outputs, predicting student performance and sentiment analysis.', 'RNNs utilize internal memory to capture sequential data', 'The introduction of Long Short Term Memory Units (LSTM) to effectively model and maintain long-term dependencies in sequential data.', 'The concept of attention overcomes limitations of traditional recurrent neural networks, capturing long-term dependencies and serving as the basis for powerful transformer models.']}