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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Building and training artificial neural networks (regression or classification) using the genetic algorithm.
| Date | Stars |
|---|---|
| 2026-07-31 | 256 |
| 2026-08-05 | 256 |
| 2026-08-06 | 256 |
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# NeuralGenetic: Training Neural Networks using the Genetic Algorithm [NeuralGenetic](https://github.com/ahmedfgad/NeuralGenetic) is a Python project for training neural networks using the genetic algorithm. [NeuralGenetic](https://github.com/ahmedfgad/NeuralGenetic) is part of the [PyGAD](https://pypi.org/project/pygad) library which is an open-source Python 3 library for implementing the genetic algorithm and optimizing machine learning algorithms. Both regression and classification neural networks are supported starting from PyGAD 2.7.0. Check documentation of the [NeuralGenetic](https://github.com/ahmedfgad/NeuralGenetic) project in the PyGAD's documentation: https://pygad.readthedocs.io/en/latest/gann.html The library is under active development and more features are added regularly. If you want a feature to be supported, please check the **Contact Us** section to send a request. # Donation - [Credit/Debit Card](https://donate.stripe.com/eVa5kO866elKgM0144): https://donate.stripe.com/eVa5kO866elKgM0144 - [Open Collective](https://opencollective.com/pygad): [opencollective.com/pygad](https://opencollective.com/pygad) - PayPal: Use either this link: [paypal.me/ahmedfgad](https://paypal.me/ahmedfgad) or the e-mail address [email protected] - Interac e-Transfer: Use e-mail address [email protected] # Tutorial Project **IMPORTANT** If you are coming for the code of the tutorial titled [**Artificial Neural Networks Optimization using Genetic Algorithm with Python**](https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad), then it has been moved to the [Tutorial Project](https://github.com/ahmedfgad/NeuralGenetic/tree/master/Tutorial Project) directory on 15 May 2020. # Installation To install [PyGAD](https://pypi.org/project/pygad), simply use pip to download and install the library from [PyPI](https://pypi.org/project/pygad) (Python Package Index). The library is at PyPI at this page https://pypi.org/project/pygad. Install PyGAD with the following command: ```python pip install pygad ``` To get started with PyGAD, please read the documentation at [Read The Docs](https://pygad.readthedocs.io/) https://pygad.readthedocs.io. # PyGAD Source Code The source code of the PyGAD' modules is found in the following GitHub projects: - [pygad](https://github.com/ahmedfgad/GeneticAlgorithmPython): (https://github.com/ahmedfgad/GeneticAlgorithmPython) - [pygad.nn](https://github.com/ahmedfgad/NumPyANN): https://github.com/ahmedfgad/NumPyANN - [pygad.gann](https://github.com/ahmedfgad/NeuralGenetic): https://github.com/ahmedfgad/NeuralGenetic - [pygad.cnn](https://github.com/ahmedfgad/NumPyCNN): https://github.com/ahmedfgad/NumPyCNN - [pygad.gacnn](https://github.com/ahmedfgad/CNNGenetic): https://github.com/ahmedfgad/CNNGenetic - [pygad.kerasga](https://github.com/ahmedfgad/KerasGA): https://github.com/ahmedfgad/KerasGA - [pygad.torchga](https://github.com/ahmedfgad/TorchGA): https://github.com/ahmedfgad/TorchGA The documentation of PyGAD is available at [Read The Docs](https://pygad.readthedocs.io/) https://pygad.readthedocs.io. # PyGAD Documentation The documentation of the PyGAD library is available at [Read The Docs](https://pygad.readthedocs.io) at this link: https://pygad.readthedocs.io. It discusses the modules supported by PyGAD, all its classes, methods, attribute, and functions. For each module, a number of examples are given. If there is an issue using PyGAD, feel free to post at issue in this [GitHub repository](https://github.com/ahmedfgad/GeneticAlgorithmPython) https://github.com/ahmedfgad/GeneticAlgorithmPython or by sending an e-mail to [email protected]. If you built a project that uses PyGAD, then please drop an e-mail to [email protected] with the following information so that your project is included in the documentation. - Project title - Brief description - Preferably, a link that directs the readers to your project Please check the **Cont
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:153452ee67940dc3, topic:deep-learning