Module 03: Sequence Models & the Attention Mechanism

Chapter Overview

This chapter traces one of the most important arcs in deep learning history: the journey from recurrent neural networks to the attention mechanism. We begin with the workhorse of early sequence modeling, the RNN, and uncover why its sequential nature creates both mathematical and practical bottlenecks. Then we introduce the attention mechanism, the breakthrough idea that lets a model learn where to look in a source sequence rather than compressing everything into a single fixed vector. Finally, we formalize attention using the query, key, value framework and build multi-head attention, the engine that powers the Transformer architecture you will study in Module 04.

Understanding this progression is essential. You cannot fully appreciate why Transformers revolutionized NLP without first understanding the limitations they were designed to overcome. Each section builds directly on the last, and by the end of this chapter you will have implemented attention from scratch and be ready to assemble the full Transformer.

Learning Objectives

Explain how RNNs process sequences and why vanishing gradients limit their effectiveness on long sequences
Describe the gating mechanisms of LSTM and GRU cells and explain how they mitigate the vanishing gradient problem
Derive Bahdanau additive attention and Luong dot-product attention, and explain how backpropagation flows through the attention layer
Define the query, key, value abstraction and compute scaled dot-product attention from first principles
Implement multi-head self-attention in PyTorch, including causal masking for autoregressive generation
Analyze the O(n²) complexity of self-attention and explain why it limits context length

Prerequisites

Module 00: Backpropagation, chain rule, gradient descent, PyTorch basics
Module 01: Word embeddings, distributional semantics, vector representations of text
Module 02: Tokenization, subword vocabularies, input representation pipelines
Linear Algebra: Matrix multiplication, softmax, dot products, projections

Chapter Overview

Learning Objectives

Prerequisites

Sections