Chapter 6: Attention Mechanisms
Master the attention mechanism from Bahdanau alignment scores to the full Transformer architecture. Learn self-attention, multi-head attention, positional encoding, and the encoder-decoder framework that powers modern NLP.
Chapter Overview
Attention mechanisms solved the fundamental bottleneck of sequence-to-sequence models: compressing an entire input sequence into a single fixed-length vector. Instead of forcing the encoder to pack everything into one representation, attention allows the decoder to dynamically focus on different parts of the input at each generation step. This simple idea --- letting the model learn where to look --- transformed machine translation and laid the groundwork for the Transformer.
The Transformer architecture, introduced in "Attention Is All You Need" (Vaswani et al., 2017), took this further by replacing recurrence entirely with self-attention. Each token in a sequence attends to every other token, computing relevance scores via query-key-value projections. Multi-head attention runs several attention functions in parallel, capturing different types of relationships. Combined with positional encodings, layer normalization, and residual connections, this produces a highly parallelizable architecture that scales to massive datasets.
This chapter traces the evolution from additive (Bahdanau) attention through scaled dot-product self-attention to the full Transformer encoder-decoder. You will also study the major variants --- BERT, GPT, and T5 --- that adapt the Transformer to different tasks by choosing which blocks to keep and how to mask attention.
Chapter Roadmap
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Attention Basics
Alignment scores, softmax weighting, and context vectors — the core attention computation.
Self-attention and parallel heads for richer representations
Self-Attention
Query-key-value projections and scaled dot-product attention within a single sequence.
Multi-Head Attention
Running parallel attention heads to capture different relationship types simultaneously.
Positional Encoding
Injecting position information into permutation-invariant attention via sinusoidal or learned encodings.
Assembling components into complete models
Transformer Architecture
Residual connections, layer normalization, and FFN layers assembled into the full Transformer.
Transformer Variants
BERT (encoder-only), GPT (decoder-only), T5 (encoder-decoder), and efficient approximations.
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