Part III: Sequence Models and Recurrent Networks

Learning from sequential data

Overview

Part III explores how neural networks handle sequential data—text, speech, time series, and more. While CNNs excel at spatial patterns, recurrent networks excel at temporal patterns and dependencies across time.

Chapters

#	Chapter	Key Concept
11	The Unreasonable Effectiveness of RNNs	RNNs can generate text, code, and more
12	Understanding LSTM Networks	Gated memory solves vanishing gradients
13	RNN Regularization	Dropout for recurrent connections
14	Relational Recurrent Neural Networks	Self-attention in recurrence

The Evolution

Vanilla RNNs      →  Simple but limited memory
       ↓
LSTMs/GRUs        →  Gated memory, long-range dependencies
       ↓
Regularized RNNs  →  Better generalization
       ↓
Attention + RNNs  →  Toward Transformers

Key Takeaway

Recurrent networks process sequences by maintaining hidden state—a form of memory that evolves over time. The challenge is making this memory effective over long sequences.

Prerequisites

Part II foundations (helpful for understanding architecture)
Basic understanding of backpropagation
Familiarity with language modeling concepts

What You’ll Be Able To Do After Part III

Understand how RNNs process sequences
Implement and train LSTM networks
Apply proper regularization to RNNs
See the path toward attention mechanisms (Part IV)