MIT 6.S191: Recurrent Neural Networks, Transformers

In this MIT Introduction to Deep Learning lecture, Ava Amini covers Recurrent Neural Networks (RNNs) and Transformers, focusing on sequence modeling problems. The lecture builds on the foundational concepts introduced in the first lecture, exploring how neural networks can handle sequential data.

The lecture is divided into several key sections:

1. **Introduction and Sequence Modeling**: Ava introduces the concept of sequence modeling, explaining its importance in various domains such as language, audio, medical readings, financial markets, and biological sequences. She emphasizes the need for models that can handle sequential data and introduces the idea of neurons with recurrence.

2. **Recurrent Neural Networks (RNNs)**: Ava explains the core idea of RNNs, which is to use a hidden state that is updated at each time step and passed forward through the sequence. This allows the network to maintain a memory of previous time steps and capture dependencies in the data. She details the mathematical formulation of RNNs, including the recurrence relation and the process of unfolding RNNs over time.

3. **Training RNNs**: The lecture covers how to train RNNs using backpropagation through time (BPTT). Ava discusses the challenges of training RNNs, such as the vanishing and exploding gradient problems, and introduces strategies to mitigate these issues, including the use of Long Short-Term Memory (LSTM) networks.

4. **Applications of RNNs**: Ava highlights various applications of RNNs, including music generation, sentiment analysis, and sequence classification. She provides examples and discusses the strengths and limitations of RNNs in these contexts.

5. **Attention Mechanism**: Ava introduces the concept of attention, which allows models to identify and focus on the most important parts of the input sequence. She explains the intuition behind attention, its relationship to search, and how attention scores are computed using query, key, and value matrices. The lecture also covers the softmax function and how attention weights are used to extract relevant features.

6. **Transformers**: The lecture transitions to Transformers, a type of neural network architecture that relies on self-attention mechanisms. Ava explains how Transformers eliminate the need for sequential processing by attending to all parts of the input simultaneously. She discusses the scalability and efficiency of Transformers and their applications in natural language processing, biology, and computer vision.

7. **Summary**: The lecture concludes with a summary of the key points covered, emphasizing the importance of sequence modeling, the strengths and limitations of RNNs, and the power of attention mechanisms and Transformers.

Throughout the lecture, Ava provides detailed explanations, mathematical formulations, and practical examples to illustrate the concepts. The lecture is designed to equip students with a deep understanding of sequence modeling and the advanced techniques used in modern deep learning architectures.