Module 5Transformer Deep Dive
Transformer Block Anatomy
Understand residuals, norms, and feed-forward layers as one optimization unit.
Why this module matters
Most transformer engineering is really about making deep residual stacks train reliably.
Prerequisites
- ▸ Multi-head attention
Learning objectives
- ▸ Explain pre-norm vs post-norm
- ▸ Understand FFN width and capacity
- ▸ Assemble a minimal block
Core concepts
Residual paths
LayerNorm placement
Feed-forward expansion
Hands-on practice
- ▸ Build a transformer block and test on fake data
Expected output
A reusable transformer block implementation.
Study checklist
- ✅ Explain pre-norm vs post-norm
- ✅ Understand FFN width and capacity
- ✅ Assemble a minimal block
Common mistakes
- ⚠️ Wrong residual ordering
- ⚠️ Ignoring normalization placement
- ⚠️ Underestimating FFN compute
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.