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This commit implements the pedagogically optimal "inevitable discovery" module progression based on expert validation and educational design principles. ## Module Reordering Summary **Previous Order (Problems)**: - 05_losses → 06_autograd → 07_dataloader → 08_optimizers → 09_spatial → 10_training - Issues: Autograd before optimizers, DataLoader before training, scattered dependencies **New Order (Beautiful Progression)**: - 05_losses → 06_optimizers → 07_autograd → 08_training → 09_spatial → 10_dataloader - Benefits: Each module creates inevitable need for the next ## Pedagogical Flow Achieved **05_losses** → "Need systematic weight updates" → **06_optimizers** **06_optimizers** → "Need automatic gradients" → **07_autograd** **07_autograd** → "Need systematic training" → **08_training** **08_training** → "MLPs hit limits on images" → **09_spatial** **09_spatial** → "Training is too slow" → **10_dataloader** ## Technical Changes ### Module Directory Renaming - `06_autograd` → `07_autograd` - `07_dataloader` → `10_dataloader` - `08_optimizers` → `06_optimizers` - `10_training` → `08_training` - `09_spatial` → `09_spatial` (no change) ### System Integration Updates - **MODULE_TO_CHECKPOINT mapping**: Updated in tito/commands/export.py - **Test directories**: Renamed module_XX directories to match new numbers - **Documentation**: Updated all references in MD files and agent configurations - **CLI integration**: Updated next-steps suggestions for proper flow ### Agent Configuration Updates - **Quality Assurance**: Updated module audit status with new numbers - **Module Developer**: Updated work tracking with new sequence - **Documentation**: Updated MASTER_PLAN_OF_RECORD.md with beautiful progression ## Educational Benefits 1. **Inevitable Discovery**: Each module naturally leads to the next 2. **Cognitive Load**: Concepts introduced exactly when needed 3. **Motivation**: Students understand WHY each tool is necessary 4. **Synthesis**: Everything flows toward complete ML systems understanding 5. **Professional Alignment**: Matches real ML engineering workflows ## Quality Assurance - ✅ All CLI commands still function - ✅ Checkpoint system mappings updated - ✅ Documentation consistency maintained - ✅ Test directory structure aligned - ✅ Agent configurations synchronized **Impact**: This reordering transforms TinyTorch from a collection of modules into a coherent educational journey where each step naturally motivates the next, creating optimal conditions for deep learning systems understanding.
36 lines
1.3 KiB
Markdown
36 lines
1.3 KiB
Markdown
# 🔥 TinyTorch: Build ML Systems from Scratch
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## 🚧 Coming Soon from Harvard University
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**TinyTorch** is an educational deep learning framework currently under development at Harvard University. This package will teach students to build complete ML systems from first principles.
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### 🎯 What's Coming
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- **Complete Tensor Operations** - N-dimensional arrays with automatic differentiation
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- **Neural Network Layers** - Linear, CNN, attention, and transformer blocks
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- **Training Infrastructure** - Optimizers, loss functions, and training loops
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- **Educational Modules** - 14+ progressive learning modules
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- **Production Tools** - CLI, testing, and deployment utilities
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### 📚 Educational Philosophy
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Most courses teach you to USE frameworks. TinyTorch teaches you to UNDERSTAND them by building every component from scratch using only NumPy.
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### 🚀 Stay Updated
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- **Repository**: [github.com/VJ/TinyTorch](https://github.com/VJ/TinyTorch)
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- **Course**: Harvard CS 287r - Machine Learning Systems
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- **Instructor**: [Prof. Vijay Janapa Reddi](https://vijay.seas.harvard.edu)
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### 📦 Installation (Placeholder)
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```bash
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pip install tinytorch
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```
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Currently installs a placeholder. Full framework coming soon!
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---
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**Build Small. Go Deep. Understand ML Systems.** ⚡
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