Commit Graph
7 Commits
Author SHA1 Message Date
Vijay Janapa Reddi bc634c586f Restructure TinyTorch into three-part learning journey (17 modules)
- Part I: Foundations (Modules 1-5) - Build MLPs, solve XOR
- Part II: Computer Vision (Modules 6-11) - Build CNNs, classify CIFAR-10
- Part III: Language Models (Modules 12-17) - Build transformers, generate text

Key changes:
- Renamed 05_dense to 05_networks for clarity
- Moved 08_dataloader to 07_dataloader (swap with attention)
- Moved 07_attention to 13_attention (Part III)
- Renamed 12_compression to 16_regularization
- Created placeholder dirs for new language modules (12,14,15,17)
- Moved old modules 13-16 to temp_holding for content migration
- Updated README with three-part structure
- Added comprehensive documentation in docs/three-part-structure.md

This structure gives students three natural exit points with concrete achievements at each level.
2025-09-22 09:50:48 -04:00
Vijay Janapa Reddi 9d637e80ef refactor: Implement learner-focused module progression with better naming
 Renamed modules for clearer pedagogical flow:
- 05_networks → 05_dense (multi-layer dense/fully connected networks)
- 06_cnn → 06_spatial (convolutional networks for spatial patterns)
- 06_attention → 07_attention (attention mechanisms for sequences)

 Shifted remaining modules down by 1:
- 07_dataloader → 08_dataloader
- 08_autograd → 09_autograd
- 09_optimizers → 10_optimizers
- 10_training → 11_training
- 11_compression → 12_compression
- 12_kernels → 13_kernels
- 13_benchmarking → 14_benchmarking
- 14_mlops → 15_mlops
- 15_capstone → 16_capstone

 Updated module metadata (module.yaml files):
- Updated names, descriptions, dependencies
- Fixed prerequisite chains and enables relationships
- Updated export paths to match new names

New learner progression:
Foundation → Individual Layers → Dense Networks → Spatial Networks → Attention Networks → Training Pipeline

Perfect pedagogical flow: Build one layer → Stack dense layers → Add spatial patterns → Add attention mechanisms → Learn to train them all.
2025-07-18 00:12:50 -04:00
Vijay Janapa Reddi 2b148e0ff3 docs: Clean up whitespace and formatting in module READMEs
- Fixed trailing whitespace in several module README files
- Ensures consistent formatting across all documentation
2025-07-16 11:50:23 -04:00
Vijay Janapa Reddi 4f9c6e40bd refactor: Implement YAML-based difficulty and time system
- Added educational metadata (difficulty, time_estimate) to all module.yaml files
- Updated convert_readmes.py to read from YAML instead of hardcoded mappings
- Standardized difficulty progression: 🥷
- Fixed path resolution for YAML reading in book build process
- Eliminated duplication: single source of truth for educational metadata
- Capstone gets special ninja treatment (🥷) as beyond-expert level
2025-07-16 11:48:09 -04:00
Vijay Janapa Reddi 383c0f138f Standardize all 14 module READMEs with consistent structure
 Complete standardization of all TinyTorch module READMEs:

📊 **Module Info**: Consistent difficulty, time, prerequisites, next steps
🎯 **Learning Objectives**: Clear, measurable, action-oriented outcomes
🧠 **Pedagogical Framework**: Build → Use → [Context-specific verb]
📚 **What You'll Build**: Concrete code examples and implementations
🚀 **Getting Started**: Prerequisites check + development workflow
🧪 **Testing**: Comprehensive test coverage + inline feedback
🎯 **Key Concepts**: Real-world applications + technical foundations
🎉 **Ready to Build**: Motivational + grid cards for all modules

 All 14 modules now follow identical structure:
- 01_setup: Foundation workflow mastery
- 02_tensor: Core data structures
- 03_activations: Neural network fundamentals
- 04_layers: Building blocks
- 05_networks: Architecture design
- 06_cnn: Computer vision foundations
- 07_dataloader: Data pipeline engineering
- 08_autograd: Automatic differentiation
- 09_optimizers: Learning algorithms
- 10_training: End-to-end orchestration
- 11_compression: Model optimization
- 12_kernels: Performance optimization
- 13_benchmarking: Systematic evaluation
- 14_mlops: Production deployment (capstone)

🎓 **Student Experience**: Predictable navigation, clear expectations, motivational flow
👨‍🏫 **Instructor Experience**: Professional consistency, easy maintenance, coherent course

This establishes the single source of truth that will automatically convert to
clean website chapters via book/convert_readmes.py
2025-07-16 01:44:49 -04:00
Vijay Janapa Reddi 9203957d07 Generate notebook files from Python modules for direct access 2025-07-15 23:51:56 -04:00
Vijay Janapa Reddi b34f3681dd Renumber modules from 00-13 to 01-14 for natural numbering
 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
2025-07-15 18:51:36 -04:00