Part IV: Attention and Transformers

The attention revolution

Overview

Part IV covers the most transformative development in deep learning since CNNs: attention mechanisms and Transformers. These architectures fundamentally changed how we process sequences, enabling parallelization, better long-range dependencies, and the foundation for modern LLMs.

Chapters

#	Chapter	Key Concept
15	Neural Machine Translation with Attention	Attention solves the bottleneck
16	Attention Is All You Need (Transformers)	Eliminating recurrence entirely
17	The Annotated Transformer	Implementation walkthrough

The Evolution

Seq2Seq (2014)    →  Encoder-decoder with bottleneck
       ↓
Attention (2015)  →  Soft alignment, no bottleneck
       ↓
Transformers (2017) →  Attention is all you need
       ↓
Modern LLMs       →  GPT, BERT, and beyond

Key Takeaway

Attention mechanisms allow models to dynamically focus on relevant parts of the input, eliminating the need for fixed-length bottlenecks and enabling parallel processing of sequences.

Prerequisites

Part III (RNNs) - helpful for understanding the problems attention solves
Understanding of sequence-to-sequence models
Familiarity with matrix operations

What You’ll Be Able To Do After Part IV

Understand how attention mechanisms work
Implement Transformer architectures
See how modern LLMs are built
Appreciate why Transformers replaced RNNs for most tasks