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📚 Update intro.md to reflect current 16-module structure

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🔄 Module Structure Updates:
- Updated from 15 to 16 modules in course journey
- Fixed module names: Networks → Dense, CNNs → Spatial
- Added new 07_attention module to Building Blocks section
- Updated all subsequent module numbering (8-16)

🎨 Section Improvements:
- Renamed 'Production & Performance' → 'Inference & Serving' (more accurate)
- Added 16_capstone to final section with 'advanced framework engineering'
- Updated descriptions to include attention mechanisms and capstone project

📊 Accurate Course Progression:
- Foundation: 01-03 (Setup, Tensors, Activations)
- Building Blocks: 04-07 (Layers, Dense, Spatial, Attention)
- Training Systems: 08-11 (DataLoader, Autograd, Optimizers, Training)
- Inference & Serving: 12-16 (Compression, Kernels, Benchmarking, MLOps, Capstone)

Result: Book intro now accurately reflects the current repository structure
and improved section naming for better clarity.
This commit is contained in:
Vijay Janapa Reddi
2025-07-18 08:54:30 -04:00
parent 21acd21308
commit cf8e4b6e7d
1 changed files with 8 additions and 8 deletions
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16
book/intro.md
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@@ -14,7 +14,7 @@ html_meta:
# Tiny🔥Torch
## Build your own Machine Learning framework from scratch.
## Build your own ML framework. Start small. Go deep.
**Most ML education teaches you to _use_ frameworks. TinyTorch teaches you to _build_ them.**
@@ -99,7 +99,7 @@ This pattern repeats for every component: tensors, layers, optimizers, even MLOp
---
## 📚 **Course Journey: 15 Modules**
## 📚 **Course Journey: 16 Modules**
```{admonition} 🏗️ Foundation
:class: note
@@ -110,23 +110,23 @@ Understanding workflow, multi-dimensional arrays, and the mathematical functions
```{admonition} 🧱 Building Blocks
:class: note
**4. Layers** • **5. Networks** • **6. CNNs**
**4. Layers** • **5. Dense** • **6. Spatial** • **7. Attention**
Dense layers, sequential architecture, and convolutional operations for computer vision.
Dense layers, sequential networks, convolutional operations, and self-attention mechanisms.
```
```{admonition} 🎯 Training Systems
:class: note
**7. DataLoader** • **8. Autograd** • **9. Optimizers** • **10. Training**
**8. DataLoader** • **9. Autograd** • **10. Optimizers** • **11. Training**
CIFAR-10 loading, automatic differentiation, SGD/Adam optimizers, and complete training orchestration.
```
```{admonition} ⚡ Production & Performance
```{admonition} 🚀 Inference & Serving
:class: note
**11. Compression** • **12. Kernels** • **13. Benchmarking** • **14. MLOps**
**12. Compression** • **13. Kernels** • **14. Benchmarking** • **15. MLOps** • **16. Capstone**
Model optimization, high-performance operations, systematic evaluation, and production monitoring.
Model optimization, high-performance operations, systematic evaluation, production monitoring, and advanced framework engineering.
```
```{admonition} 🎓 Capstone Project