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Code Issues 7 Packages Projects Releases Wiki Activity

📚 Comprehensive README update to match current repository structure

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🔧 Module Structure Updates:
- Updated from 15 to 16 modules throughout documentation
- Fixed module names: 05_networks → 05_dense, 06_cnn → 06_spatial
- Added 07_attention module to documentation and flowchart
- Corrected module numbering in all sections (Deep Learning now 06-10, Production 11-15)

📊 Course Organization:
- Updated repository structure diagram with correct module names
- Fixed mermaid flowchart to show actual module dependencies
- Updated capstone references (15 core modules → 15 core modules + capstone = 16 total)
- Corrected learning path recommendations (core modules 01-10 for foundations)

📦 Package References:
- Added exports for dense.py, spatial.py, attention.py in tinytorch/core/
- Updated all module counts and difficulty progressions
- Fixed references to complete framework capabilities

Result: README now accurately reflects the actual 16-module structure with
correct naming, dependencies, and learning progression. No more confusion
between documentation and actual repository state.
This commit is contained in:
Vijay Janapa Reddi
2025-07-18 08:19:17 -04:00
parent 098d237a4a
commit e00d0824c3
1 changed files with 58 additions and 52 deletions
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@@ -104,7 +104,7 @@ tito nbdev build # Update package
```
TinyTorch/
├── modules/source/ # 15 educational modules
├── modules/source/ # 16 educational modules
│ ├── 01_setup/ # Development environment setup
│ │ ├── module.yaml # Module metadata
│ │ ├── README.md # Learning objectives and guide
@@ -115,22 +115,26 @@ TinyTorch/
│ │ └── tensor_dev.py
│ ├── 03_activations/ # Neural network activation functions
│ ├── 04_layers/ # Dense layers and transformations
│ ├── 05_networks/ # Sequential networks and MLPs
│ ├── 06_cnn/ # Convolutional neural networks
│ ├── 07_dataloader/ # Data loading and preprocessing
│ ├── 08_autograd/ # Automatic differentiation
│ ├── 09_optimizers/ # SGD, Adam, learning rate scheduling
│ ├── 10_training/ # Training loops and validation
│ ├── 11_compression/ # Model optimization and compression
│ ├── 12_kernels/ # High-performance operations
│ ├── 13_benchmarking/ # Performance analysis and profiling
│ ├── 14_mlops/ # Production monitoring and deployment
│ └── 15_capstone/ # Systems engineering capstone project
│ ├── 05_dense/ # Sequential networks and MLPs
│ ├── 06_spatial/ # Convolutional neural networks
│ ├── 07_attention/ # Self-attention and transformer components
│ ├── 08_dataloader/ # Data loading and preprocessing
│ ├── 09_autograd/ # Automatic differentiation
│ ├── 10_optimizers/ # SGD, Adam, learning rate scheduling
│ ├── 11_training/ # Training loops and validation
│ ├── 12_compression/ # Model optimization and compression
│ ├── 13_kernels/ # High-performance operations
│ ├── 14_benchmarking/ # Performance analysis and profiling
│ ├── 15_mlops/ # Production monitoring and deployment
│ └── 16_capstone/ # Systems engineering capstone project
├── tinytorch/ # Your built framework package
│ ├── core/ # Core implementations (exported from modules)
│ │ ├── tensor.py # Generated from 02_tensor
│ │ ├── activations.py # Generated from 03_activations
│ │ ├── layers.py # Generated from 04_layers
│ │ ├── dense.py # Generated from 05_dense
│ │ ├── spatial.py # Generated from 06_spatial
│ │ ├── attention.py # Generated from 07_attention
│ │ └── ... # All your implementations
│ └── utils/ # Shared utilities and tools
├── book/ # Interactive course website
@@ -152,7 +156,7 @@ TinyTorch/
---
## 📚 Complete Course: 15 Modules
## 📚 Complete Course: 16 Modules
**Difficulty Progression:** ⭐ Beginner → ⭐⭐ Intermediate → ⭐⭐⭐ Advanced → ⭐⭐⭐⭐ Expert → ⭐⭐⭐⭐⭐🥷 Capstone
@@ -161,65 +165,67 @@ TinyTorch/
* **02_tensor**: N-dimensional arrays and tensor operations
* **03_activations**: ReLU, Sigmoid, Tanh, Softmax functions
* **04_layers**: Dense layers and matrix operations
* **05_networks**: Sequential networks and MLPs
* **05_dense**: Sequential networks and MLPs
### **🧠 Deep Learning** (Modules 06-09)
* **06_cnn**: Convolutional neural networks and image processing
* **07_dataloader**: Data loading, batching, and preprocessing
* **08_autograd**: Automatic differentiation and backpropagation
* **09_optimizers**: SGD, Adam, and learning rate scheduling
### **🧠 Deep Learning** (Modules 06-10)
* **06_spatial**: Convolutional neural networks and image processing
* **07_attention**: Self-attention and transformer components
* **08_dataloader**: Data loading, batching, and preprocessing
* **09_autograd**: Automatic differentiation and backpropagation
* **10_optimizers**: SGD, Adam, and learning rate scheduling
### **⚡ Systems & Production** (Modules 10-14)
* **10_training**: Training loops, metrics, and validation
* **11_compression**: Model pruning, quantization, and distillation
* **12_kernels**: Performance optimization and custom operations
* **13_benchmarking**: Profiling, testing, and performance analysis
* **14_mlops**: Monitoring, deployment, and production systems
### **⚡ Systems & Production** (Modules 11-15)
* **11_training**: Training loops, metrics, and validation
* **12_compression**: Model pruning, quantization, and distillation
* **13_kernels**: Performance optimization and custom operations
* **14_benchmarking**: Profiling, testing, and performance analysis
* **15_mlops**: Monitoring, deployment, and production systems
### **🎓 Capstone Project** (Module 15)
* **15_capstone**: Capstone project applying systems engineering skills
### **🎓 Capstone Project** (Module 16)
* **16_capstone**: Advanced framework engineering specialization tracks
**Status**: All 15 modules complete with inline tests and educational content
**Status**: All 16 modules complete with inline tests and educational content
---
## 🔗 **Complete System Integration**
**This isn't 15 isolated assignments.** Every component you build integrates into one cohesive, fully functional ML framework:
**This isn't 16 isolated assignments.** Every component you build integrates into one cohesive, fully functional ML framework:
```mermaid
flowchart TD
A[01_setup<br/>Setup & Environment] --> B[02_tensor<br/>Core Tensor Operations]
B --> C[03_activations<br/>ReLU, Sigmoid, Tanh]
C --> D[04_layers<br/>Dense Layers]
D --> E[05_networks<br/>Sequential Networks]
D --> E[05_dense<br/>Sequential Networks]
E --> F[06_cnn<br/>Convolutional Networks]
E --> G[07_dataloader<br/>Data Loading]
B --> H[08_autograd<br/>Automatic Differentiation]
H --> I[09_optimizers<br/>SGD & Adam]
E --> F[06_spatial<br/>Convolutional Networks]
E --> G[07_attention<br/>Self-Attention]
F --> H[08_dataloader<br/>Data Loading]
B --> I[09_autograd<br/>Automatic Differentiation]
I --> J[10_optimizers<br/>SGD & Adam]
F --> J[10_training<br/>Training Loops]
G --> J
I --> J
H --> K[11_training<br/>Training Loops]
G --> K
J --> K
J --> K[11_compression<br/>Model Optimization]
J --> L[12_kernels<br/>High-Performance Ops]
J --> M[13_benchmarking<br/>Performance Analysis]
J --> N[14_mlops<br/>Production Monitoring]
K --> L[12_compression<br/>Model Optimization]
K --> M[13_kernels<br/>High-Performance Ops]
K --> N[14_benchmarking<br/>Performance Analysis]
K --> O[15_mlops<br/>Production Monitoring]
K --> O[15_capstone<br/>Systems Engineering]
L --> O
M --> O
N --> O
L --> P[16_capstone<br/>Framework Engineering]
M --> P
N --> P
O --> P
```
### **🎯 How It All Connects**
**Foundation (01-05):** Build your core data structures and basic operations
**Deep Learning (06-09):** Add neural networks and automatic differentiation
**Production (10-14):** Scale to real applications with training and production systems
**Mastery (15):** Optimize and extend your complete framework
**Deep Learning (06-10):** Add neural networks and automatic differentiation
**Production (11-15):** Scale to real applications with training and production systems
**Mastery (16):** Optimize and extend your complete framework
**The Result:** A complete, working ML framework built entirely by you, capable of:
- ✅ Training CNNs on CIFAR-10 with 90%+ accuracy
@@ -229,7 +235,7 @@ flowchart TD
### **🚀 Capstone: Optimize Your Framework**
After completing the 14 core modules, you have a **complete ML framework**. The final challenge: make it better through systems engineering.
After completing the 15 core modules, you have a **complete ML framework**. The final challenge: make it better through systems engineering.
**Choose Your Focus:**
- ⚡ **Performance Engineering**: GPU kernels, vectorization, memory-efficient operations
@@ -344,7 +350,7 @@ git push origin main # Triggers documentation deployment
```
TinyTorch/
├── modules/source/XX/ # 14 source modules with inline tests
├── modules/source/XX/ # 16 source modules with inline tests
├── tinytorch/core/ # Your exported ML framework
├── tito/ # CLI and course management tools
├── book/ # Jupyter Book source and config
@@ -506,8 +512,8 @@ Reading about neural networks: Building neural networks:
**You can work at your own pace:**
- **Quick exploration:** 1-2 modules to understand the approach
- **Focused learning:** Core modules (01-08) for solid foundations
- **Complete mastery:** All 15 modules for full framework expertise
- **Focused learning:** Core modules (01-10) for solid foundations
- **Complete mastery:** All 16 modules for full framework expertise
Each module is self-contained, so you can stop and start as needed.
</details>