MIT 6.S191 (2023): Recurrent Neural Networks, Transformers, and Attention

MIT Introduction to Deep Learning 6.S191: Lecture 2
Recurrent Neural Networks
Lecturer: Ava Amini
2023 Edition
For all lectures, slides, and lab materials: introtodeeplearning.com
Lecture Outline
0:00​ - Introduction
3:07​ - Sequence modeling
5:09​ - Neurons with recurrence
12:05 - Recurrent neural networks
13:47 - RNN intuition
15:03​ - Unfolding RNNs
18:57 - RNNs from scratch
21:50 - Design criteria for sequential modeling
23:45 - Word prediction example
29:57​ - Backpropagation through time
32:25 - Gradient issues
37:03​ - Long short term memory (LSTM)
39:50​ - RNN applications
44:50 - Attention fundamentals
48:10 - Intuition of attention
50:30 - Attention and search relationship
52:40 - Learning attention with neural networks
58:16 - Scaling attention and applications
1:02:02 - Summary
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Over all videos on KZbin that explained about Transformer architecture (including the visual explanation) , this is the BEST EXPLANATION ever done. Simple, contextual, high level, step by step complexity progression. Thank you the educators and MIT!

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One of the best lectures I have seen on Sequence Models, with crystal clear explanations! :)

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Indeed commendable the way this lecture has been ordered and difficult topic like self-attention has been lucidly explained. Thanks to the instructors, really appreciated.

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Extremely informative, well structured and paced. A pleasure to watch and follow. Thank you.

[@hamza-325]

I watched and read a lot of content about Transformers and never understood what are those three Q, K, and V vectors doing so I coulnd't understand how attention works, until today when I watched this lecture doing the analogy of KZbin search and the Iron Man picture. Now it became much much clearer! Thanks for the brilliant analogies that you are making!

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[@gemini_537]

Summary by Gemini: The lecture is about recurrent neural networks, transformers, and attention. The speaker, Ava, starts the lecture by introducing the concept of sequential data and how it is different from the data that we typically work with in neural networks. She then goes on to discuss the different types of sequential modeling problems, such as text generation, machine translation, and image captioning. Next, Ava introduces the concept of recurrent neural networks (RNNs) and how they can be used to process sequential data. She explains that RNNs are able to learn from the past and use that information to make predictions about the future. However, she also points out that RNNs can suffer from vanishing and exploding gradients, which can make them difficult to train. To address these limitations, Ava introduces the concept of transformers. Transformers are a type of neural network that does not rely on recurrence. Instead, they use attention to focus on the most important parts of the input data. Ava explains that transformers have been shown to be very effective for a variety of sequential modeling tasks, including machine translation and text generation. In the last part of the lecture, Ava discusses the applications of transformers in various fields, such as biology, medicine, and computer vision. She concludes the lecture by summarizing the key points and encouraging the audience to ask questions.

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This girl looks so young

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@@ukaszkasprzak5921 Kognitywistyka UW Zagadnienia z AI, machine learningu i matematyki są tu omawiane obok zagadnień humanistycznych: Lingwistyka, Filozofia Umysłu, Psychologia Poznawcza etc. Radzę przejrzeć Program studiów, proste googlowanie wystarczy

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50:30 - Attention mechnaism beautifully explained. Thank you #AvaAmini

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Wow, Transformers, and Attention was an absolute lifesaver! 🚀🙌 The explanations were crystal clear, and I finally have a solid grasp on these concepts. This video saved me so much time and confusion. Huge thanks to the Ava for making such an informative and engaging tutorial! Can't wait to delve deeper into the world of AI and machine learning. 🤖💡

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This is my favorite subject :) (following is self clarification of said words that feel exaggerated) 4:08 - binary classification or filtering is a sequence of steps: - new recording - retrieval of a constant record - compare new and constant record - express a property of the compare process So, sequencing really is a property of maybe all systems. While "wave sequencing" is built on top of a Sequencer System, that repeatedly uses the "same actions" per sequence element.

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Best end to the lecture: “Thank you for your attention.” ❤😂

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ty to MIT for giving back a little in an impactful way

[@mostinho7]

15:05 we have different weights matrix for generating h_t and generating y_t h_t generated using two different weights matrix, to take contribution from previous state and current input 51:20 start of attention explanation 59:30 each attention head focus on some part similar to how each filter in cnn can learn to extract specific features like horizontal lines etc

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The most intutive explanation of Self Attention I have seen!

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How beautifully explained. Loved it 🥰

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Great Presentation @8:00 minutes it really explained a circuitry I was looking forward to exploring

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Came here to refresh my memory of deep learning for sequential data. I really like how Ava brings us from one algorithm to another. It makes perfect sense to me.

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Are there the labs available as well?

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Thank you so much for the free course. Benifit and appreciate

[@Reaperaxe9]

Fully understand transformers. One of the clearest and succinct explanations out there, so intuitive. Thank you!!

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Wonderful, easy to focus and understand :). Great quality! Grateful that this is open source!

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Simply brilliant!

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I already have some knowledge on the subject, however, I like to keep myself updated and there is always something new to learn. She clearly explains how what she is teaching really works. The whole video is worth watching.

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Lovely presentation! It couldn't get more interesting!

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Thank you Ava Soleimany and MIT ☺😊🤗💜

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I have used LSTM and Transformer a lot, but I can still get more insights from this lecture.

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amazing lecture series, thanks for sharing this knowledge with the world. I am curious if theres a lecture on LSTM'S

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Thank you for this amazing and easy to understand course! I'm a beginner of the RNN, but I can almost know all the concepts from this lecture!

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I have many years of lecturing experience and just wish I was as competent she is. Great job.

[@nazrinnagori]

query key value pairs always put me off whener I start to learn about transformers, this time I actually finished the video. Thanks MIT

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Thanks for sharing!

[@mohadreza9419]

Mr Amini thanks for your channel

[@eee8]

Great Teamwork of Alex Amini and Ava Amini.

[@excitingtomorrow]

Your explanation of attention took me 2 revisits to this video to truly truly understand! But now when I did, my love for deep learning got stronger :)

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oh epochs=3 rofl

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legendary lecture, thank you for sharing

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Very intuitive explanation, thanks!

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Thank you@MIT

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This was the third video I watched in search of understanding what transformers are, and by far the best one. Thanks.

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She is fantastic at teaching. I love how easily understandable she makes it. Thank you, Prof Amini.

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best Friday after-work fun thanks!

[@bohanwang-nt7qz]

🎯Course outline for quick navigation: [00:09-02:02]Sequence modeling with neural networks -[00:09-00:37]Ava introduces second lecture on sequence modeling in neural networks. -[00:55-01:46]The lecture aims to demystify sequential modeling by starting from foundational concepts and developing intuition through step-by-step explanations. [02:02-13:24]Sequential data processing and modeling -[02:02-02:46]Sequential data is all around us, from sound waves to text and language. -[03:10-03:50]Sequential modeling can be applied to classification and regression problems, with feed-forward models operating in a fixed, static setting. -[05:02-05:26]Lecture covers building neural networks for recurrent and transformer architectures. -[11:56-12:37]Rnn captures cyclic temporal dependency in maintaining and updating state at each time step. [13:24-20:04]Understanding rnn computation -[14:40-15:04]Explains rnn's prediction for next word, updating state, and processing sequential information. -[15:05-15:47]Rnn computes hidden state update and output prediction. -[16:17-17:05]Rnn updates hidden state and generates output in single operation. -[18:45-19:39]The total loss for a particular input to the rnn is computed by summing individual loss terms. the rnn implementation in tensorflow involves defining an rnn as a layer operation and class, initializing weight matrices and hidden state, and passing forward through the rnn network to process a given input x. [20:05-29:13]Rnn in tensorflow -[20:05-20:54]Tensorflow abstracts rnn network definition for efficiency. practice rnn implementation in today's lab. -[21:16-21:43]Today's software lab focuses on many-to-many processing and sequential modeling. -[22:53-23:21]Sequence implies order, impacting predictions. parameter sharing is crucial for effective information processing. -[25:04-25:29]Language must be numerically represented for processing, requiring translation into a vector. -[28:29-28:56]Predict next word with short, long, and even longer sequences while tracking dependencies across different lengths. [29:14-41:53]Rnn training and issues -[30:02-30:27]Training neural network models using backpropagation algorithm for sequential information. -[30:45-31:43]Rnns use backpropagation through time to adjust network weights and minimize overall loss through individual time steps. -[32:03-32:57]Repeated multiplications of big weight matrices can lead to exploding gradients, making it infeasible to train the network stably. -[35:45-37:18]Three ways to mitigate vanishing gradient problem: change activation functions, initialize parameters, use a more robust version of recurrent neural unit. -[36:13-37:01]Relu activation function helps mitigate vanishing gradient problem by maintaining derivatives greater than one, and weight initialization with identity matrices prevents rapid shrinkage of weight updates. -[37:54-38:25]Lstms are effective at tracking long-term dependencies by controlling information flow through gates. -[40:18-41:13]Build rnn to predict musical notes and generate new sequences, e.g. completing schubert's unfinished symphony. [41:53-50:11]Challenges in rnn and self-attention -[43:58-44:40]Rnns face challenges in slow processing and limited capacity for long memory data. -[46:37-47:00]Concatenate all time steps into one vector input for the model -[47:21-47:45]Feed-forward network lacks scalability, loses in-order information, and hinders long-term memory. -[48:11-48:34]Self-attention is a powerful concept in deep learning and ai, foundational in transformer architecture. -[48:58-49:25]Exploring the power of self-attention in neural networks, focusing on attending to important parts of an input example. [50:13-56:20]Neural network attention mechanism -[50:13-50:43]Understanding the concept of search and its role in extracting important information from a larger data set. -[51:52-55:24]Neural networks use self-attention to extract relevant information, like in the example of identifying a relevant video on deep learning, by computing similarity scores between queries and keys. -[53:32-53:54]A neural network encodes positional information to process time steps all at once in singular data. -[55:32-55:57]Comparing vectors using dot product to measure similarity. [56:20-01:02:47]Self-attention mechanism in nlp -[56:20-57:14]Computing attention scores to define relationships in sequential data. -[59:11-59:39]Self-attention heads extract high attention features, forming larger network architectures. -[01:00:32-01:00:56]Self-attention is a key operation in powerful neural networks like gpt-3. offered by Coursnap

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Штоэто.запрасмоттр.непанядно

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Pretty straight forward lecture.

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I come back every year to check these lectures and to see what innovations made it into the lectures. Pleasantly surprised to see the name change, congrats!

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What do you mean by name change?

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@@agamersdiary1622 This woman got married to one of the other lecturers (the channel owner Alexander).

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Thank you so much

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[@sciencely8601]

00:16 Building neural networks for handling sequential data 03:19 Sequential data introduces new problem definitions for neural networks 10:03 Recurrent Neural Networks link computation and information via recurrent relation. 13:37 RNN processes temporal information and generates predictions. 20:22 Key criteria for designing effective RNNs 23:33 Recurrent neural networks design criteria and need for more powerful architectures. 30:08 Back propagation through time in RNN involves back propagating loss through individual time steps and handling sequential information. 33:23 Vanishing gradient problem in recurrent neural networks 40:03 RNNs used for music generation and sentiment classification 43:32 RNNs have encoding bottlenecks and processing limitations 49:45 Self-attention involves identifying important parts and extracting relevant information. 52:51 Transformers eliminate recurrence and capture positional order information through positional encoding and attention mechanism. 59:35 Self-attention heads extract salient features from data. 1:02:49 Starting work on the labs

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ThNks mit

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Thanks a lot!

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Thank you.