3 Commits

Author	SHA1	Message	Date
Vijay Janapa Reddi	8a101cf52d	Add tito grade command for simplified NBGrader interface ... Implement comprehensive grading workflow wrapped behind tito CLI: • tito grade setup - Initialize NBGrader course structure • tito grade generate - Create instructor version with solutions • tito grade release - Create student version without solutions • tito grade collect - Collect student submissions • tito grade autograde - Automatically grade submissions • tito grade manual - Open manual grading interface • tito grade feedback - Generate student feedback • tito grade export - Export grades to CSV This allows users to only learn tito commands without needing to understand NBGrader's complex interface. All grading functionality is accessible through simple, consistent tito commands.	2025-09-17 19:22:02 -04:00
Vijay Janapa Reddi	771ed98a80	🧹 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	59d58718f9	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