https://github.com/kosasih/neurosphere
A Decentralized, Self-Evolving, AI-Powered, Quantum-Resistant, Interconnected Network of Autonomous Systems
https://github.com/kosasih/neurosphere
Last synced: 8 months ago
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A Decentralized, Self-Evolving, AI-Powered, Quantum-Resistant, Interconnected Network of Autonomous Systems
- Host: GitHub
- URL: https://github.com/kosasih/neurosphere
- Owner: KOSASIH
- License: apache-2.0
- Created: 2024-08-26T03:43:27.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-21T11:33:36.000Z (over 1 year ago)
- Last Synced: 2025-04-23T07:50:48.438Z (about 1 year ago)
- Language: Python
- Size: 320 KB
- Stars: 13
- Watchers: 1
- Forks: 0
- Open Issues: 6
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# NeuroSphere
A Decentralized, Self-Evolving, AI-Powered, Quantum-Resistant, Interconnected Network of Autonomous Systems
NeuroSphere
==========
Welcome to NeuroSphere, an open-source platform for building and deploying artificial intelligence (AI) and machine learning (ML) models for IoT devices.
Overview
--------
NeuroSphere is a Python-based platform that enables developers to create, train, and deploy AI/ML models on IoT devices. The platform provides a simple and intuitive API for building and deploying models, as well as a range of tools and libraries for data processing, model training, and model deployment.
Features
--------
* **Model Building**: NeuroSphere provides a range of tools and libraries for building AI/ML models, including support for popular frameworks like TensorFlow and PyTorch.
* **Model Training**: NeuroSphere provides a range of tools and libraries for training AI/ML models, including support for distributed training and hyperparameter tuning.
* **Model Deployment**: NeuroSphere provides a range of tools and libraries for deploying AI/ML models on IoT devices, including support for edge computing and real-time inference.
* **Data Processing**: NeuroSphere provides a range of tools and libraries for processing and analyzing data from IoT devices, including support for data preprocessing, feature engineering, and data visualization.
Getting Started
--------------
To get started with NeuroSphere, follow these steps:
1. **Install NeuroSphere**: Install NeuroSphere using pip: `pip install neurosphere`
2. **Create a New Project**: Create a new NeuroSphere project using the command: `neurosphere new myproject`
3. **Build and Train a Model**: Build and train an AI/ML model using the NeuroSphere API and tools.
4. **Deploy the Model**: Deploy the trained model on an IoT device using the NeuroSphere deployment tools.
Documentation
--------------
For more information on using NeuroSphere, please see the [NeuroSphere Documentation](https://kosasih.github.io/NeuroSphere/docs/).
Community
----------
NeuroSphere is an open-source project and we welcome contributions from the community. To get involved, please see the [Contributing Guide](https://kosasih.github.io/NeuroSphere/contributing/).
License
-------
NeuroSphere is licensed under the Apache 2.0 license.
Acknowledgments
---------------
NeuroSphere is built on top of a range of open-source projects, including TensorFlow, PyTorch, and scikit-learn. We would like to acknowledge the contributions of these projects and the wider open-source community.
Contact
------
For more information on NeuroSphere, please contact us at discussion page.