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

MIT Introduction to Deep Learning 6.S191: Lecture 2
Recurrent Neural Networks
Lecturer: Ava Amini
** New 2024 Edition **

Lecture Outline
0:00​ - Introduction
3:42​ - Sequence modeling
5:30​ - Neurons with recurrence
12:20 - Recurrent neural networks
14:08 - RNN intuition
17:14​ - Unfolding RNNs
19:54 - RNNs from scratch
22:41 - Design criteria for sequential modeling
24:24 - Word prediction example
31:50​ - Backpropagation through time
33:40 - Gradient issues
37:15​ - Long short term memory (LSTM)
40:00​ - RNN applications
44:00- Attention fundamentals
46:46 - Intuition of attention
49:13 - Attention and search relationship
51:22 - Learning attention with neural networks
57:45 - Scaling attention and applications
1:00:08 - Summary
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[wolpumba]

*Abstract*

This lecture delves into the realm of sequence modeling, exploring how neural networks can effectively handle sequential data like text, audio, and time series. Beginning with the limitations of traditional feedforward models, the lecture introduces Recurrent Neural Networks (RNNs) and their ability to capture temporal dependencies through the concept of "state." The inner workings of RNNs, including their mathematical formulation and training using backpropagation through time, are explained. However, RNNs face challenges such as vanishing gradients and limited memory capacity. To address these limitations, Long Short-Term Memory (LSTM) networks with gating mechanisms are presented. The lecture further explores the powerful concept of "attention, " which allows networks to focus on the most relevant parts of an input sequence. Self-attention and its role in Transformer architectures like GPT are discussed, highlighting their impact on natural language processing and other domains. The lecture concludes by emphasizing the versatility of attention mechanisms and their applications beyond text data, including biology and computer vision.

*Sequence Modeling and Recurrent Neural Networks*
- 0:01: This lecture introduces sequence modeling, a class of problems involving sequential data like audio, text, and time series.
- 1:32: Predicting the trajectory of a moving ball exemplifies the concept of sequence modeling, where past information aids in predicting future states.
- 2:42: Diverse applications of sequence modeling are discussed, spanning natural language processing, finance, and biology.

*Neurons with Recurrence*
- 5:30: The lecture delves into how neural networks can handle sequential data.
- 6:26: Building upon the concept of perceptrons, the idea of recurrent neural networks (RNNs) is introduced.
- 7:48: RNNs address the limitations of traditional feedforward models by incorporating a "state" that captures information from previous time steps, allowing the network to model temporal dependencies.
- 10:07: The concept of "state" in RNNs is elaborated upon, representing the network's memory of past inputs.
- 12:23: RNNs are presented as a foundational framework for sequence modeling tasks.

*Recurrent Neural Networks*
- 12:53: The mathematical formulation of RNNs is explained, highlighting the recurrent relation that updates the state at each time step based on the current input and previous state.
- 14:11: The process of "unrolling" an RNN is illustrated, demonstrating how the network processes a sequence step-by-step.
- 17:17: Visualizing RNNs as unrolled networks across time steps aids in understanding their operation.
- 19:55: Implementing RNNs from scratch using TensorFlow is briefly discussed, showing how the core computations translate into code.

*Design Criteria for Sequential Modeling*
- 22:45: The lecture outlines key design criteria for effective sequence modeling, emphasizing the need for handling variable sequence lengths, maintaining memory, preserving order, and learning conserved parameters.
- 24:28: The task of next-word prediction is used as a concrete example to illustrate the challenges and considerations involved in sequence modeling.
- 25:56: The concept of "embedding" is introduced, which involves transforming language into numerical representations that neural networks can process.
- 28:42: The challenge of long-term dependencies in sequence modeling is discussed, highlighting the need for networks to retain information from earlier time steps.

*Backpropagation Through Time*
- 31:51: The lecture explains how RNNs are trained using backpropagation through time (BPTT), which involves backpropagating gradients through both the network layers and time steps.
- 33:41: Potential issues with BPTT, such as exploding and vanishing gradients, are discussed, along with strategies to mitigate them.

*Long Short Term Memory (LSTM)*
- 37:21: To address the limitations of standard RNNs, Long Short-Term Memory (LSTM) networks are introduced.
- 37:35: LSTMs employ "gating" mechanisms that allow the network to selectively retain or discard information, enhancing its ability to handle long-term dependencies.

*RNN Applications*
- 40:03: Various applications of RNNs are explored, including music generation and sentiment classification.
- 40:16: The lecture showcases a musical piece generated by an RNN trained on classical music.

*Attention Fundamentals*
- 44:00: The limitations of RNNs, such as limited memory capacity and computational inefficiency, motivate the exploration of alternative architectures.
- 46:50: The concept of "attention" is introduced as a powerful mechanism for identifying and focusing on the most relevant parts of an input sequence.

*Intuition of Attention*
- 48:02: The core idea of attention is to extract the most important features from an input, similar to how humans selectively focus on specific aspects of visual scenes.
- 49:18: The relationship between attention and search is illustrated using the analogy of searching for relevant videos on YouTube.

*Learning Attention with Neural Networks*
- 51:29: Applying self-attention to sequence modeling is discussed, where the network learns to attend to relevant parts of the input sequence itself.
- 52:05: Positional encoding is explained as a way to preserve information about the order of elements in a sequence.
- 53:15: The computation of query, key, and value matrices using neural network layers is detailed, forming the basis of the attention mechanism.

*Scaling Attention and Applications*
- 57:46: The concept of attention heads is introduced, where multiple attention mechanisms can be combined to capture different aspects of the input.
- 58:38: Attention serves as the foundational building block for Transformer architectures, which have achieved remarkable success in various domains, including natural language processing with models like GPT.
- 59:13: The broad applicability of attention beyond text data is highlighted, with examples in biology and computer vision.


i summarized the transcript with gemini 1.5 pro

[samiragh]

Can't be waiting for another extraordinary lecture. Thank you Alex and Ava.

[ERalyGainulla]

Sequence Modeling and Recurrent Neural Networks
0:01 – Введение в моделирование последовательностей: работа с временными рядами, текстом, аудио. Пример: предсказание траектории движущегося мяча.
2:42 – Примеры применения: обработка естественного языка (NLP), финансы, биология.
Neurons with Recurrence
5:30 – Как нейронные сети могут работать с последовательными данными.
6:26 – Введение рекуррентных нейронных сетей (RNN): почему их используют вместо традиционных сетей.
10:07 – Понятие состояния (state): память о предыдущих входах.
Recurrent Neural Networks
12:53 – Математическая формулировка RNN: уравнения и принципы работы.
14:11 – Развёртка RNN во времени.
17:17 – Визуализация и понимание шагов обработки последовательности.
Design Criteria for Sequential Modeling
22:45 – Основные критерии проектирования: переменная длина последовательностей, сохранение порядка, память.
24:28 – Пример: предсказание следующего слова в предложении.
Backpropagation Through Time
31:51 – Как обучаются RNN: обратное распространение через время (BPTT).
33:41 – Проблемы BPTT: затухающие и взрывающиеся градиенты.
Long Short Term Memory (LSTM)
37:21 – Введение LSTM для решения проблем стандартных RNN.
37:35 – Как работают гейты (входной, забывающий, выходной).
RNN Applications
40:03 – Примеры применения RNN: генерация музыки, классификация настроений текста.
Attention Fundamentals
44:00 – Ограничения RNN, которые мотивируют использование механизмов внимания.
46:50 – Концепция внимания: выбор ключевых частей последовательности.
Intuition of Attention
48:02 – Основная идея: внимание выбирает важные признаки, аналогично человеческому восприятию.
Learning Attention with Neural Networks
51:29 – Механизм self-attention: как сеть фокусируется на релевантных частях последовательности.
53:15 – Использование матриц Query, Key и Value для вычисления внимания.
Scaling Attention and Applications
57:46 – Многоголовые механизмы внимания (attention heads).
58:38 – Внимание как основа архитектуры Transformer: NLP, биология, компьютерное зрение.

[daniyalkabir]

These lectures are extremly high quality. Thank you :) for posting them online so that we can learn from one of the best universities in the world.

[marlhex]

Personally, I love the way Ava articulated each word and how she mapped the problem in her head. Great job

[pavalep]

Thank you for being the pioneers in teaching Deep Learning to Common folks like me :)
Thank you Alexander and Ava 👍

[shahriarahmadfahim]

Can't believe how amazingly the two lecturers squeeze so much content and explain with such clarity in an hour!

Would be great if you published the lab with the preceding lecture coz the lecture ended setting up the mood for the lab haha.

But not complaining, thanks again for such amazing stuffs!

[frankhofmann]

I'm sitting here in wonderful Berlin at the beginning of May and looking at this incredibly clear presentation! Wunderbar! And thank you very much for the clarity of your logic!

[dr.rafiamumtaz]

excellent way of explaining the deep learning concepts

[ajithdevadiga]

This is a great summarization of sequence model.
truly amazed at the aura of knowledge.

[pw]

Ava is such a talented teacher. (And Alex, too, of course.)

[jamesgambrah]

As I await the commencement of this lecture, I reflect fondly on my past experiences, which have been nothing short of excellent.

[kapardhikannekanti]

This is one of the best and engaging sessions I've ever attended. The entire hour was incredibly smooth, and I was captivated the entire time.

[DanielHinjosGarcía]

This was an amazing class and one of the clearest introductions to Sequence Models that I have ever seen. Great work!

[beAstudentnooneelse]

It's a great place to apply all learning strategies for jetpack classes, love it, I just can't wait for more and in depth knowledge.

[clivedsouza]

The intuition building was stellar, really eye opening. Thanks!

[ObaroJohnson-qv]

Very audible and confidently delivered the lecture perfectly. Thanks

[TheSauravKokane]

1. Here we are taking "h" as previous history factor or hidden state, is it single dimensional or multidimensional?
2. What is the behavior of "h" - hidden state inside the NN or inside each layer of RNN? (in a single timestamp?)
3. How is mismatch between number of input features and number of out put features is maintained? For example consider image captioning. Here we are giving fixed number of input parameters, but what will determine how many words will be generated as a caption.
Or for example consider generation of sentences related to given word, here we are giving one word as input, but what will decide length of output?

[victortg]

This was an extraordinary explanation of Transformers!

[wuyanfeng]

thank you so much. the explanation on self-attention is so clearly