Module 3Transformer Deep Dive
Multi-Head Attention
Split representation space into multiple attention subspaces.
Why this module matters
Multi-head attention is where representational diversity enters the transformer block.
Prerequisites
- ▸ Single-head attention
Learning objectives
- ▸ Reshape tensors for heads safely
- ▸ Understand head dimension vs model dimension
- ▸ Inspect parameter and compute costs
Core concepts
Projection matrices
Head concatenation
Per-head specialization
Hands-on practice
- ▸ Implement a minimal multi-head attention module
Expected output
A working multi-head attention class with shape tracing.
Study checklist
- ✅ Reshape tensors for heads safely
- ✅ Understand head dimension vs model dimension
- ✅ Inspect parameter and compute costs
Common mistakes
- ⚠️ Incorrect transpose order
- ⚠️ Mismatched head dimensions
- ⚠️ Using view instead of reshape carelessly
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 continue
The model still lacks order, so next comes positional information.
Back to course overview →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.