Deleted 5 README/documentation files with stale information:
- 01_1957_perceptron/README.md
- 02_1969_xor/README.md
- 03_1986_mlp/README.md
- 04_1998_cnn/README.md
- 05_2017_transformer/PERFORMANCE_METRICS_DEMO.md
Issues with these files:
- Wrong file names (rosenblatt_perceptron.py, train_mlp.py, train_cnn.py)
- Old paths (examples/datasets/)
- Duplicate content (already in Python file docstrings)
- Could not be kept in sync with code
Documentation now lives exclusively in comprehensive Python docstrings
at the top of each milestone file, ensuring it stays accurate and
students see rich context when running files.
Deleted vaswani_shakespeare.py and get_shakespeare() from data_manager:
- 45-60 minute training time (too slow for educational demos)
- Required external download from Karpathy's char-rnn repo
- Replaced by faster TinyTalks ChatGPT milestone (3-5 min training)
Primary transformer milestone is now vaswani_chatgpt.py:
- Uses TinyTalks Q&A dataset (already in repo)
- Fast training with clear learning signal (Q&A format)
- Better pedagogical value (students see transformer learn to chat)
Removed achievement/gamification system that was unused:
- milestone_dashboard.py (620+ lines, only 1 file used it)
- .milestone_progress.json (progress tracking data)
- perceptron_trained_v2.py (only dashboard user, duplicate of perceptron_trained.py)
Rationale:
- Dashboard was used by only 1 of 15 milestone files
- Milestones are educational stories, not standardized tests
- Achievement badges felt gimmicky for ML systems learning
- Custom Rich UI in each file is clearer and more educational
- Reduces dependencies (removed psutil system monitoring)
- Remove overly broad patterns (*_ANALYSIS.md, *_AUDIT.md)
- Make report patterns more specific (MODULE_REVIEW_REPORT_*.md)
- Add clear comments explaining why directories are ignored
- Keep dataset ignores (data/, datasets/) as they are downloaded files
Create comprehensive guidelines for git commits, code quality, testing, and development workflow that apply to Cursor, Claude, and any other AI assistants
- Document two-tier testing approach (inline vs integration)
- Explain purpose and scope of each test type
- Provide test coverage matrix for all 20 modules
- Include testing workflow for students and instructors
- Add best practices and common patterns
- Show current status: 11/15 inline tests passing, all 20 modules have test infrastructure
- Add tests/16_quantization with run_all_tests.py and integration test
- Add tests/17_compression with run_all_tests.py and integration test
- Add tests/18_acceleration with run_all_tests.py and integration test
- Add tests/19_benchmarking with run_all_tests.py and integration test
- Add tests/20_capstone with run_all_tests.py and integration test
- All test files marked as pending implementation with TODO markers
- Completes test directory structure for all 20 modules
- Rename tests/14_kvcaching to tests/14_profiling
- Rename tests/15_profiling to tests/15_memoization
- Aligns test structure with optimization tier reorganization
- Import Module base class from core.layers
- Fix embeddings import path (text.embeddings not core.embeddings)
- Fix attention import (MultiHeadAttention not SelfAttention)
- Fix transformer import path (models.transformer not core.transformers)
- Handle missing functional module gracefully with try/except
- Update __all__ exports to match available components
- Delete kvcaching_dev.py (superseded by memoization_dev.py)
- Delete kvcaching_dev.ipynb (superseded by memoization_dev.ipynb)
- memoization_dev files are the current versions with complete content
- Move logo-tinytorch-grey.png to _static/logos/
- Move logo-tinytorch-simple.png to _static/logos/
- Move logo-tinytorch-white.png to _static/logos/
- Move tensortorch.png to _static/logos/
- Update _config.yml to reference new logo path
- Keeps all logo versions organized in standard static assets location
Add build scripts and GitHub Actions workflow to support PDF generation:
- build_pdf.sh: LaTeX-based PDF build for professional quality
- build_pdf_simple.sh: HTML-to-PDF build without LaTeX requirement
- Makefile: convenient shortcuts for common build tasks
- GitHub Actions workflow: automated PDF builds on demand
Supports multiple output formats:
- HTML website (default, via jupyter-book)
- PDF via HTML-to-PDF (pyppeteer, no LaTeX needed)
- PDF via LaTeX (professional typography, requires LaTeX)
Usage:
make html - Build HTML website
make pdf-simple - Build PDF without LaTeX
make pdf - Build PDF with LaTeX
- tinytorch/benchmarking/: Benchmark class for Module 19
- tinytorch/competition/: Submission utilities for Module 20
- tinytorch/data/: Data loading utilities
- tinytorch/utils/data/: Additional data helpers
Exported from modules 19-20 and module 08
- Update module path from 15_profiling to 14_profiling
- Add quick_profile helper for quick bottleneck discovery
- Add analyze_weight_distribution for pruning analysis
- Export new helper functions in __all__
Comprehensive summary of all changes made:
- Module reorganization complete
- Chapter updates complete
- All commits made in logical pieces
- Ready for testing and review
Cleanup of renamed files:
- Deleted old module source files (14_kvcaching, 15_profiling, 16_acceleration, etc.)
- Deleted old chapter markdown files
- These have been replaced by reorganized versions in previous commits
