https://github.com/danieldacosta/gradient-descent
gradient-descent is a package that contains different gradient-based algorithms. The package purpose is to facilitate the user experience when using optimization algorithms.
https://github.com/danieldacosta/gradient-descent
gradients-descent optimization-algorithms package
Last synced: about 1 year ago
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gradient-descent is a package that contains different gradient-based algorithms. The package purpose is to facilitate the user experience when using optimization algorithms.
- Host: GitHub
- URL: https://github.com/danieldacosta/gradient-descent
- Owner: DanielDaCosta
- License: mit
- Created: 2020-04-02T19:17:35.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-07-20T20:44:27.000Z (almost 2 years ago)
- Last Synced: 2025-04-21T15:09:48.792Z (about 1 year ago)
- Topics: gradients-descent, optimization-algorithms, package
- Language: Python
- Homepage: https://pypi.org/project/gradient-descent/
- Size: 378 KB
- Stars: 10
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# gradient-descent
[](https://pypi.org/project/gradient-descent/)
gradient-descent is a package that contains different gradient-based algorithms, usually used to optimize Neural Networks and other machine learning models. The package contains the following algorithms:
- Gradients Descent
- Momentum
- RMSprop
- Nasterov accelerated gradient
- Adam
The package purpose is to facilitate the user experience when using optimization algorithms and to allow the user to have a better intuition about how these *black-boxes* algorithms work.
This is an open-source project any feedback, improvement ideas, and contributors are welcome.
## Installation
**Dependencies**
- Python (>= 3.6)
- NumPy (>= 1.13.3)
- Matplotlib (>=3.2.1)
**User installation**
```
pip install gradient-descent
```
## Development
All contributors of all levels are welcome to help in any possible away.
**Souce Code**
```
git clone https://github.com/DanielDaCosta/gradient-descent.git
```
**Tests**
```
pytest tests
```
## TO DO
The package is still on its early days and there are improvements to make. If you want to contribute to the project, you can start by addressing one of the items below:
- [ ] Build new optimization algorithms
- [ ] Extend its use for multivariable functions
- [ ] New ideas of functions for better usability
- [ ] Improve Documentation
# References & Acknowledgements
First of all I would like to thank Hammad Shaikh by his well documented and very well explained GitHub repository [Math of Machine Learning Course by Siraj](https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/blob/master/Gradient%20Descent%20for%20Optimization/Gradient%20Descent%20for%20Optimization.ipynb)
I appreciate the help of the following repos and articles:
- [Optimizing Gradient Descent](https://ruder.io/optimizing-gradient-descent/) by Sebastian Ruder
- [Optimization Techniques for Gradient Descent](https://www.geeksforgeeks.org/optimization-techniques-for-gradient-descent/?ref=rp) by www.geeksforgeeks.org website
- [optimization_algos](https://github.com/idc9/optimization_algos) GitHub repository by Iain Carmichael
- [Deep Learning](http://www.deeplearningbook.org) by Begnio, Goodfellow and Courtville