Module 6Transformer Deep Dive
Encoder Models and BERT Thinking
Learn bidirectional contextual encoding for classification and retrieval.
Why this module matters
Encoder models solve a different problem from generative decoder models.
Prerequisites
- ▸ Transformer block
Learning objectives
- ▸ Explain masked language modeling
- ▸ Use CLS-style classification heads
- ▸ Compare pooled vs token-level outputs
Core concepts
Bidirectional context
Masked objectives
Sentence representations
Hands-on practice
- ▸ Train a tiny masked-token encoder on toy data
Expected output
A minimal BERT-style experiment.
Study checklist
- ✅ Explain masked language modeling
- ✅ Use CLS-style classification heads
- ✅ Compare pooled vs token-level outputs
Common mistakes
- ⚠️ Assuming encoder and decoder pretraining are interchangeable
Module rhythm
- 1. Read the summary and why-it-matters section first.
- 2. Work through concepts before rushing into practice.
- 3. Use the checklist to verify real understanding, not just completion.
How to use this page well
Treat each module as a compact learning system: understand the intuition, verify the concepts, do one hands-on task, then use the checklist and mistakes section to pressure-test your understanding.