https://github.com/redx94/dynamic-neural-network-refinement
Dynamic Neural Network Refinement (DNNR) is an advanced framework that allows neural networks to adapt in real time. Unlike static systems, DNNR refines network parameters on-the-fly to optimize performance. Its modularity ensures easy customization for versatile applications.
https://github.com/redx94/dynamic-neural-network-refinement
adaptive-systems advanced-neural-architectures ai-research-and-development ai-scalability artificial-intelligence customizable-ai data-driven-ai deep-learning dynamic-learning-models intelligent-systems machine-learning machine-learning-framework network-parameter-tuning neural-networks on-the-fly-refinement performance-optimization real-time-optimization versatile-ai-solutions
Last synced: 3 months ago
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Dynamic Neural Network Refinement (DNNR) is an advanced framework that allows neural networks to adapt in real time. Unlike static systems, DNNR refines network parameters on-the-fly to optimize performance. Its modularity ensures easy customization for versatile applications.
- Host: GitHub
- URL: https://github.com/redx94/dynamic-neural-network-refinement
- Owner: redx94
- License: agpl-3.0
- Created: 2024-11-24T04:36:06.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-02-24T00:00:15.000Z (3 months ago)
- Last Synced: 2025-02-24T00:25:20.589Z (3 months ago)
- Topics: adaptive-systems, advanced-neural-architectures, ai-research-and-development, ai-scalability, artificial-intelligence, customizable-ai, data-driven-ai, deep-learning, dynamic-learning-models, intelligent-systems, machine-learning, machine-learning-framework, network-parameter-tuning, neural-networks, on-the-fly-refinement, performance-optimization, real-time-optimization, versatile-ai-solutions
- Language: Python
- Homepage:
- Size: 23.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Security: SECURITY.md
- Roadmap: docs/roadmap.md
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README
# Dynamic Neural Network Refinement
[](https://github.com/redx94/Dynamic-Neural-Network-Refinement/actions)
[](https://www.gnu.org/licenses/agpl-3.0)
[](https://github.com/psf/black)
[](https://www.python.org/downloads/)
> Self-evolving neural networks that adapt in real-time based on data complexity
## 🚀 Overview
Dynamic Neural Network Refinement (DNNR) revolutionizes deep learning by enabling neural networks to autonomously adapt their architectures based on real-time data complexity. Unlike traditional static models, DNNR networks evolve during both training and inference, optimizing themselves for better performance and efficiency.
## ✨ Key Features
- 🔄 **Real-time Architecture Adaptation**: Networks automatically adjust their structure based on data complexity
- 📈 **Performance-Driven Evolution**: Continuous optimization using metrics like variance, entropy, and sparsity
- 🔌 **Easy Integration**: Seamless integration with existing PyTorch projects
- 🚅 **Distributed Training**: Built-in support for multi-GPU and multi-node training
- 📊 **Advanced Monitoring**: Prometheus + Grafana dashboards for real-time insights
- 🔒 **Production-Ready**: Comprehensive testing, CI/CD, and security measures
## 🛠️ Installation
Get started with a few simple commands:
```bash
# Clone the repository
git clone https://github.com/redx94/Dynamic-Neural-Network-Refinement.git
cd Dynamic-Neural-Network-Refinement
# Create and activate a virtual environment (optional but recommended)
python3 -m venv venv
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
```
## 🚀 Quick Start Guide
After installation, kick off the dynamic refinement process with:
```bash
python run_refinement.py --config config/example_config.json
```
Customize the provided configuration to tailor the refinement process to your specific requirements. Detailed usage instructions and parameter descriptions are available in our [Documentation](docs/).
## 📚 Documentation
For in-depth tutorials, API references, and advanced configurations, check out our:
- [Wiki](https://github.com/redx94/Dynamic-Neural-Network-Refinement/wiki)
- [Docs Directory](docs/)
## 🤝 Contributing
We welcome your contributions! Here’s how to join the revolution:
1. **Fork the Repository:**
Click the "Fork" button at the top-right of this page.
2. **Create a Feature Branch:**
```bash
git checkout -b feature/your-feature-name
```
3. **Commit Your Changes:**
```bash
git commit -am 'Add new feature'
```
4. **Push and Open a PR:**
```bash
git push origin feature/your-feature-name
```
Then, open a pull request for review.
For more details, see our [CONTRIBUTING](CONTRIBUTING.md) guidelines.
## 📜 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## 📞 Get in Touch
Have questions, suggestions, or need support? Reach out to us:
- **Email:** [email protected]
- **GitHub Issues:** [Submit an Issue](https://github.com/redx94/Dynamic-Neural-Network-Refinement/issues)
## 🙏 Acknowledgments
- Special thanks to the vibrant community of AI researchers and developers driving innovation every day.
- Inspired by the latest breakthroughs in dynamic neural architectures and adaptive AI systems.
**Dynamic Neural Network Refinement** is your gateway to next-level neural networks that evolve, adapt, and optimize continuously. Join us on this journey into the future of AI!