Clean, dependency-driven organization:
- Part I (1-5): MLPs for XORNet
- Part II (6-10): CNNs for CIFAR-10
- Part III (11-15): Transformers for TinyGPT
Key improvements:
- Dropped modules 16-17 (regularization/systems) to maintain scope
- Moved normalization to module 13 (Part III where it's needed)
- Created three CIFAR-10 examples: random, MLP, CNN
- Each part introduces ONE major innovation (FC → Conv → Attention)
CIFAR-10 now showcases progression:
- test_random_baseline.py: ~10% (random chance)
- train_mlp.py: ~55% (no convolutions)
- train_cnn.py: ~60%+ (WITH Conv2D - shows why convolutions matter!)
This follows actual ML history and each module is needed for its capstone.
✅ Rename all module directories: 00_setup → 01_setup, etc.
✅ Update convert_modules.py mappings for new directory names
✅ Update _toc.yml file paths and titles (1-14 instead of 0-13)
✅ Regenerate all overview pages with new numbering
✅ Fix all broken references in usage-paths and intro
✅ Update chapter references to use natural numbering
Benefits:
- More intuitive course progression starting from 1
- Matches academic course numbering conventions
- Eliminates confusion about 'Module 0' concept
- Cleaner mental model for students and instructors
- All references and links properly updated
Complete transformation: 14 modules now numbered 01-14
