- Change --all flag meaning from 'clean both file types' to 'clean all modules'
- Make clean command consistent with test and export commands
- Require explicit module name or --all flag (no implicit behavior)
- Update help text and examples
- Now supports both:
- tito module clean tensor (specific module)
- tito module clean --all (all modules)
- Update test, export, and clean commands to use positional arguments
- Change from 'tito module test --module dataloader' to 'tito module test dataloader'
- Eliminates redundant --module flag within module command group
- Update help text and examples to reflect new syntax
- Maintains backward compatibility with --all flag
- More intuitive and consistent CLI design
- Add Module Info sections with difficulty ratings to all README.md files
- Use consistent 4-star difficulty scale: ⭐ Beginner, ⭐⭐ Intermediate, ⭐⭐⭐ Advanced, ⭐⭐⭐⭐ Expert
- Include time estimates, prerequisites, and next steps for each module
- Maintain clear separation: README.md = student experience, module.yaml = system metadata
- Difficulty progression: Setup (⭐) → Tensor/Activations/Layers (⭐⭐) → Networks/CNN/DataLoader (⭐⭐⭐) → Transformer (⭐⭐⭐⭐)
- Help students plan their learning journey and set appropriate expectations
Introduces a comprehensive module for 2D Convolutional Neural Networks.
This module provides a foundational understanding of CNNs through:
- Implementation of a naive Conv2D layer with sliding window convolution
- Visualization of kernel operations and feature map construction
- Composition of Conv2D layers with other layers to build a simple ConvNet
This structure provides a step-by-step guide to building and understanding CNNs, with clear examples and tests.
Enhances the notebook by replacing some unicode characters with more standard and universally compatible symbols, improving the overall readability and user experience.
- Add template section to tensor, layers, activations, and cnn modules
- Create docs/development/module-template.md for future reference
- Clarify learning vs building structure consistently
- Show students where their code will live in the final package
- Decouple learning modules from production organization
- Add cnn_dev.py with NBDev educational pattern, Conv2D for-loop TODO, and all scaffolding
- Add README.md explaining learning goals, what is implemented vs provided, and rationale
- Add tests/test_cnn.py for basic correctness and shape tests
- Generate cnn_dev.ipynb for notebook workflow
