Commit Graph

9 Commits

Author SHA1 Message Date
Vijay Janapa Reddi
b441541c28 Add structural organization headers to 04_layers module
- Added ## 🔧 DEVELOPMENT section before Step 1 where development begins
- Added ## 🤖 AUTO TESTING section before nbgrader block
- Updated to ## 🎯 MODULE SUMMARY: Neural Network Layers

Improves notebook organization without changing any code logic or content.
2025-07-20 09:56:48 -04:00
Vijay Janapa Reddi
a7692e6c91 🧹 Remove backup files - Clean repository maintenance
- Delete 8 *_backup.py files from modules/source directories
- Remove tito/commands/test.py.backup file
- Eliminates obsolete backup files from version control
- Keeps repository clean and focused on current implementations
- Reduces repository size and improves maintainability

Removed files:
- modules/source/02_tensor/tensor_dev_backup.py
- modules/source/03_activations/activations_dev_backup.py
- modules/source/04_layers/layers_dev_backup.py
- modules/source/05_dense/dense_dev_backup.py
- modules/source/06_spatial/spatial_dev_backup.py
- modules/source/08_dataloader/dataloader_dev_backup.py
- modules/source/09_autograd/autograd_dev_backup.py
- modules/source/13_kernels/kernels_dev_backup.py
- tito/commands/test.py.backup
2025-07-20 08:42:59 -04:00
Vijay Janapa Reddi
dff09a99f2 🧹 Remove Jupyter notebooks from modules/source - Python-first workflow
- Delete all 15 .ipynb files from modules/source directories
- Align with TinyTorch's Python-first development philosophy
- .py files are the source of truth, .ipynb files are temporary outputs
- Prevents version control conflicts with notebook metadata
- Students work directly with .py files using Jupytext format
- Notebooks can be regenerated when needed via 'tito nbdev generate'

Removed files:
- All *_dev.ipynb files across modules 01-15
- Keeps repository clean and focused on source code
2025-07-20 08:41:26 -04:00
Vijay Janapa Reddi
c8d5ed74b8 🧠 Core ML: Standardize test naming in neural network building blocks
- Activations: test_integration_* → test_module_* (module dependency tests)
- Layers: test_matrix_multiplication → test_unit_matrix_multiplication
- Layers: test_dense_layer → test_unit_dense_layer
- Layers: test_layer_activation → test_unit_layer_activation
- Dense: test_integration_* → test_module_* (module dependency tests)
- Spatial: test_integration_* → test_module_* (module dependency tests)
- Attention: test_integration_* → test_module_* (module dependency tests)
- Establishes unit vs module test distinction for neural network components
2025-07-20 08:39:00 -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
50d2d63d31 Standardize module headers - consistent 🔥 emoji and clean chapter titles
README Updates:
- All modules now use consistent '🔥 Module: [Name]' format
- Removed inconsistent emojis (🧠, 🚀, 📊, 🧱, 🏋️)
- Removed module numbers and descriptive subtitles
- Clean, consistent branding across all 14 modules

Converter Updates:
- Added header cleaning logic to strip module prefixes from chapter titles
- Chapters now show clean names: 'CNN', 'Tensor', 'Setup', etc.
- No emoji or module numbers in final website headers
- Maintains clean, professional appearance

Result: Consistent source files + clean website presentation
2025-07-16 01:18:07 -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