Module 3PyTorch Foundations
Building Models with nn.Module
Turn loose tensor math into reusable trainable modules.
Why this module matters
Real projects need code you can inspect, reuse, and extend, not one-off forward passes hidden in notebooks.
Prerequisites
- ▸ Autograd basics
- ▸ Python classes
Learning objectives
- ▸ Register parameters and submodules correctly
- ▸ Design clear forward methods
- ▸ Split big models into reusable blocks
Core concepts
Parameter registration
Module composition
Forward semantics
Hands-on practice
- ▸ Implement an MLP and inspect named_parameters
- ▸ Refactor repeated code into a reusable block
- ▸ Count parameters and identify the largest submodule
Expected output
A clean module-based classifier skeleton ready for training.
Study checklist
- ✅ Register parameters and submodules correctly
- ✅ Design clear forward methods
- ✅ Split big models into reusable blocks
Common mistakes
- ⚠️ Creating layers inside forward
- ⚠️ Forgetting super().__init__
- ⚠️ Shadowing module attributes with plain tensors
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
Once models are structured, learn how loss and optimization actually move parameters.
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.