Module 9PyTorch Foundations
Capstone: CIFAR-10 CNN Classifier
Integrate data, model, optimization, logging, and export into one coherent project.
Why this module matters
This is where isolated concepts become an engineering workflow you can trust and reuse.
Prerequisites
- ▸ All previous PyTorch modules
Learning objectives
- ▸ Train a robust CIFAR-10 classifier
- ▸ Track metrics and analyze errors
- ▸ Export and validate the trained model
Core concepts
End-to-end training workflow
Error analysis
Model export and parity checks
Hands-on practice
- ▸ Train to target accuracy
- ▸ Build confusion-matrix analysis
- ▸ Export TorchScript and run parity tests
Expected output
A complete image-classification project suitable for portfolio and deployment experiments.
Study checklist
- ✅ Train a robust CIFAR-10 classifier
- ✅ Track metrics and analyze errors
- ✅ Export and validate the trained model
Common mistakes
- ⚠️ Chasing accuracy before validating the pipeline
- ⚠️ No baseline comparison
- ⚠️ Ignoring export correctness
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
You are now ready to move into sequence modeling and transformer systems.
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.