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Restructure course to start with hands-on Module 0: Setup

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- Moved Introduction to "Course Orientation" section (no longer Module 0)
- Renumbered all modules: Setup becomes Module 0, course now has 16 modules
- Updated table of contents to separate orientation from formal course modules
- Updated intro.md and vision.md to reflect 16 modules instead of 17
- Course now starts immediately with hands-on implementation (Setup)
- Maintains Build→Use→Reflect philosophy by removing non-implementation module
- Introduction remains accessible as orientation material without being numbered module
This commit is contained in:
Vijay Janapa Reddi
2025-09-16 10:12:33 -04:00
parent adc6521c1f
commit 8c5921a6d0
3 changed files with 33 additions and 30 deletions
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39
book/_toc.yml
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@@ -17,54 +17,57 @@ parts:
- file: instructor-guide
title: "📚 Instructor Guide"
- caption: Foundation
- caption: Course Orientation
chapters:
- file: chapters/00-introduction
title: "0. Introduction"
title: "Introduction"
- caption: Foundation
chapters:
- file: chapters/01-setup
title: "1. Setup"
title: "0. Setup"
- file: chapters/02-tensor
title: "2. Tensors"
title: "1. Tensors"
- file: chapters/03-activations
title: "3. Activations"
title: "2. Activations"
- caption: Building Blocks
chapters:
- file: chapters/04-layers
title: "4. Layers"
title: "3. Layers"
- file: chapters/05-dense
title: "5. Dense"
title: "4. Dense"
- file: chapters/06-spatial
title: "6. Spatial"
title: "5. Spatial"
- file: chapters/07-attention
title: "7. Attention"
title: "6. Attention"
- caption: Training Systems
chapters:
- file: chapters/08-dataloader
title: "8. DataLoader"
title: "7. DataLoader"
- file: chapters/09-autograd
title: "9. Autograd"
title: "8. Autograd"
- file: chapters/10-optimizers
title: "10. Optimizers"
title: "9. Optimizers"
- file: chapters/11-training
title: "11. Training"
title: "10. Training"
- caption: Inference & Serving
chapters:
- file: chapters/12-compression
title: "12. Compression"
title: "11. Compression"
- file: chapters/13-kernels
title: "13. Kernels"
title: "12. Kernels"
- file: chapters/14-benchmarking
title: "14. Benchmarking"
title: "13. Benchmarking"
- file: chapters/15-mlops
title: "15. MLOps"
title: "14. MLOps"
- caption: Capstone Project
chapters:
- file: chapters/16-capstone
title: "16. Capstone"
title: "15. Capstone"
- caption: Additional Resources
chapters:

22
book/intro.md
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@@ -147,39 +147,39 @@ After TinyTorch, you'll be the person your team asks:
---
## 📚 **Course Journey: 17 Modules**
## 📚 **Course Journey: 16 Modules**
```{admonition} 🏗️ Foundation
:class: note
**0. Introduction** • **1. Setup** • **2. Tensors** • **3. Activations**
**0. Setup** • **1. Tensors** • **2. Activations**
System overview, development workflow, multi-dimensional arrays, and mathematical functions that enable learning.
Development workflow, multi-dimensional arrays, and mathematical functions that enable learning.
```
```{admonition} 🧱 Building Blocks
:class: note
**4. Layers** • **5. Dense** • **6. Spatial** • **7. Attention**
**3. Layers** • **4. Dense** • **5. Spatial** • **6. Attention**
Dense layers, sequential networks, convolutional operations, and self-attention mechanisms with memory analysis.
```
```{admonition} 🎯 Training Systems
:class: note
**8. DataLoader** • **9. Autograd** • **10. Optimizers** • **11. Training**
**7. DataLoader** • **8. Autograd** • **9. Optimizers** • **10. Training**
CIFAR-10 loading, automatic differentiation with graph management, SGD/Adam with memory profiling, and complete training orchestration.
```
```{admonition} 🚀 Production Systems
:class: note
**12. Compression** • **13. Kernels** • **14. Benchmarking** • **15. MLOps**
**11. Compression** • **12. Kernels** • **13. Benchmarking** • **14. MLOps**
Model optimization, high-performance operations, systematic evaluation, and production monitoring with real deployment patterns.
```
```{admonition} 🎓 Capstone Project
:class: note
**16. Integration Engineering**
**15. Integration Engineering**
Choose your specialization: performance optimization, algorithm extensions, systems engineering, benchmarking analysis, or developer tools.
```
@@ -188,18 +188,18 @@ Choose your specialization: performance optimization, algorithm extensions, syst
## 🔗 **Complete System Integration**
**This isn't 14 separate exercises.** Every component you build integrates into one fully functional ML framework:
**This isn't 15 separate exercises.** Every component you build integrates into one fully functional ML framework:
```{admonition} 🎯 How It All Connects
:class: important
**Module 2: Your Tensor class** → **Module 3: Powers your activation functions** → **Module 4: Enables your layers** → **Module 5: Forms your networks** → **Module 8: Drives your autograd system** → **Module 9: Optimizes with your SGD/Adam** → **Module 10: Trains on real CIFAR-10 data**
**Module 1: Your Tensor class** → **Module 2: Powers your activation functions** → **Module 3: Enables your layers** → **Module 4: Forms your networks** → **Module 7: Drives your autograd system** → **Module 8: Optimizes with your SGD/Adam** → **Module 9: Trains on real CIFAR-10 data**
**Result:** A complete, working ML framework that you built from scratch, capable of training real neural networks on real datasets.
```
### **🎯 Capstone: Optimize Your Framework**
After completing the 14 core modules, you have a **complete ML framework**. Now make it better through systems engineering:
After completing the 15 core modules, you have a **complete ML framework**. Now make it better through systems engineering:
**Choose Your Focus:**
- ⚡ **Performance Optimization**: GPU kernels, vectorization, memory-efficient operations
@@ -228,7 +228,7 @@ After completing the 14 core modules, you have a **complete ML framework**. Now
*"I want to build this myself"*
- Fork the repo and work locally with full development environment
- Build complete ML framework from scratch with `tito` CLI
- 14 progressive assignments from setup to production MLOps
- 15 progressive assignments from setup to production MLOps
- Professional development workflow with automated testing
### **👨‍🏫 [Classroom Use](usage-paths/classroom-use.md)** *(Instructors)*

2
book/vision.md
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@@ -155,7 +155,7 @@ ML Systems Thinking sections in every module:
- Connection to industry practices and framework design decisions
**🎯 Complete Integration**
- 17 modules that build ONE cohesive ML framework
- 16 modules that build ONE cohesive ML framework
- Each component integrates with others (not isolated exercises)
- Professional package structure with CLI tools and testing