ML Algorithm Playground 🧪

Core ML algorithm implementations with GPU acceleration. Featuring optimized implementations across various libraries with comprehensive analysis.

Features • Installation • Quick Start • Documentation • Contributing

📑 Table of Contents

• Features
• Project Structure
• Prerequisites
• Installation
• Quick Start
• Documentation
  • Algorithms
  • Optimization
  • Benchmarks
• Contributing
• Versioning
• Authors
• Citation
• License
• Acknowledgments

✨ Features

• Classical ML algorithm implementations
• GPU-accelerated training
• Performance optimization techniques
• Comparative analysis tools
• Interactive examples and tutorials

📁 Project Structure

graph TD
    A[ml-algorithm-playground] --> B[algorithms]
    A --> C[optimization]
    A --> D[examples]
    A --> E[benchmarks]
    B --> F[supervised]
    B --> G[unsupervised]
    B --> H[ensemble]
    C --> I[gpu]
    C --> J[parallel]
    D --> K[notebooks]
    D --> L[tutorials]
    E --> M[performance]
    E --> N[comparison]

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ml-algorithm-playground/
├── algorithms/         # Algorithm implementations
│   ├── supervised/    # Supervised learning
│   ├── unsupervised/ # Unsupervised learning
│   └── ensemble/     # Ensemble methods
├── optimization/      # Optimization utilities
│   ├── gpu/          # GPU acceleration
│   └── parallel/     # Parallel processing
├── examples/         # Example implementations
├── benchmarks/       # Performance tests
├── tests/           # Unit tests
└── README.md        # Documentation

🔧 Prerequisites

• Python 3.8+
• CUDA 11.8+
• scikit-learn 1.3+
• XGBoost 2.0+
• NVIDIA GPU (optional)

📦 Installation

# Clone repository
git clone https://github.com/BjornMelin/ml-algorithm-playground.git
cd ml-algorithm-playground

# Create environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

🚀 Quick Start

from ml_playground import algorithms, optimization

# Initialize model with GPU support
model = algorithms.GradientBoosting(
    gpu_acceleration=True,
    n_estimators=100
)

# Train model with automatic optimization
trained_model = optimization.train_with_gpu(
    model,
    X_train,
    y_train,
    optimization_level='O2'
)

# Make predictions
predictions = trained_model.predict(X_test)

📚 Documentation

Algorithms

Algorithm	Type	GPU Support	Performance Gain
XGBoost	Gradient Boosting	✓	10x
K-Means	Clustering	✓	5x
SVM	Classification	✓	8x
Random Forest	Ensemble	✓	4x

Optimization

• GPU acceleration
• Multi-threading
• Memory optimization
• CUDA kernels

Benchmarks

Algorithm performance comparison:

Dataset Size	Algorithm	CPU Time	GPU Time	Speedup
1M samples	XGBoost	45s	5s	9x
500K samples	K-Means	30s	6s	5x
2M samples	Random Forest	120s	30s	4x

🤝 Contributing

• Contributing Guidelines
• Code of Conduct
• Development Guide

📌 Versioning

We use SemVer for versioning. For available versions, see the tags on this repository.

✍️ Authors

Bjorn Melin

• GitHub: @BjornMelin
• LinkedIn: Bjorn Melin

📝 Citation

@misc{melin2024mlalgorithmplayground,
  author = {Melin, Bjorn},
  title = {ML Algorithm Playground: GPU-Accelerated Machine Learning Implementations},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/BjornMelin/ml-algorithm-playground}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• scikit-learn developers
• XGBoost team
• NVIDIA for CUDA support
• Open source ML community

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Made with 🧪 and ❤️ by Bjorn Melin