TinyTorch/examples

TinyTorch Examples 🔥

Real-world examples showing what you can build with TinyTorch!

What Are These Examples?

These are real ML applications written using TinyTorch just like you would use PyTorch. Each example:

• Uses import tinytorch as a real package
• Shows professional ML code patterns
• Demonstrates actual capabilities you've built
• Can be run by anyone to see TinyTorch in action

Running Examples

# After installing/building TinyTorch:
cd examples/xor_network/
python train.py

# Or for image recognition:
cd examples/mnist_recognition/
python train_mnist.py

Example Categories

🧠 Neural Network Fundamentals

• xor_network/ - Classic XOR problem with hidden layers
• linear_regression/ - Simple regression tasks

👁️ Computer Vision

• mnist_recognition/ - Handwritten digit recognition
• cifar10_classifier/ - Real-world object classification
• image_filters/ - Convolutional feature extraction

🤖 Language & Generation

• text_generation/ - Generate text with TinyGPT
• code_completion/ - Python code generation

⚡ Advanced Topics

• autograd_demo/ - Automatic differentiation
• optimization_comparison/ - SGD vs Adam
• model_compression/ - Quantization and pruning
• performance_profiling/ - Benchmarking tools

Example Structure

Each example directory contains:

example_name/
├── train.py          # Main training script
├── model.py          # Model architecture (if complex)
├── README.md         # What this example demonstrates
├── requirements.txt  # Just needs: tinytorch
└── config.yml        # Metadata about the example

For Students

After completing each module, the corresponding example will work:

• Module 05 → xor_network/ works
• Module 08 → mnist_recognition/ works
• Module 11 → cifar10_classifier/ works with training
• Module 16 → text_generation/ works

For Everyone Else

Want to see what TinyTorch can do? Just run any example:

# See a neural network learn XOR:
python examples/xor_network/train.py

# Train a CNN on real images:
python examples/cifar10_classifier/train.py

# Generate text with a transformer:
python examples/text_generation/generate.py

---

These aren't toy demos - they're real ML applications using a framework built from scratch!