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https://diffsharp.github.io/DiffSharp/
DiffSharp: Differentiable Functional Programming
https://diffsharp.github.io/DiffSharp/
autodiff deep-learning dotnet gpu machine-learning neural-network tensor
Last synced: 8 days ago
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DiffSharp: Differentiable Functional Programming
- Host: GitHub
- URL: https://diffsharp.github.io/DiffSharp/
- Owner: DiffSharp
- License: bsd-2-clause
- Created: 2014-08-28T16:19:45.000Z (about 10 years ago)
- Default Branch: dev
- Last Pushed: 2024-04-15T20:25:41.000Z (7 months ago)
- Last Synced: 2024-08-02T08:09:50.539Z (3 months ago)
- Topics: autodiff, deep-learning, dotnet, gpu, machine-learning, neural-network, tensor
- Language: F#
- Homepage: http://diffsharp.github.io
- Size: 162 MB
- Stars: 579
- Watchers: 39
- Forks: 67
- Open Issues: 37
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-dotnet-datascience - DiffSharp - DiffSharp allows for exact and efficient calculation of derivatives, by systematically invoking the chain rule of calculus at the elementary operator level during program execution. (Machine Learning and Differential Programming)
README
-----------------------------------------
[](https://github.com/DiffSharp/DiffSharp/actions)
[](https://coveralls.io/github/DiffSharp/DiffSharp?branch=)
This is the development branch of DiffSharp 1.0.
> **NOTE: This branch is undergoing development. It has incomplete code, functionality, and design that are likely to change without notice; when using TorchSharp backend, only x64 platform is currently supported out of the box, see [DEVGUIDE.md] for more details.**
DiffSharp is a tensor library with support for [differentiable programming](https://en.wikipedia.org/wiki/Differentiable_programming). It is designed for use in machine learning, probabilistic programming, optimization and other domains.
**Key features**
* Nested and mixed-mode differentiation
* Common optimizers, model elements, differentiable probability distributions
* F# for robust functional programming
* PyTorch familiar naming and idioms, efficient LibTorch CUDA/C++ tensors with GPU support
* Linux, macOS, Windows supported
* Use interactive notebooks in Jupyter and Visual Studio Code
* 100% open source
## Documentation
You can find the documentation [here](https://diffsharp.github.io/), including information on installation and getting started.
Release notes can be found [here](https://github.com/DiffSharp/DiffSharp/blob/dev/RELEASE_NOTES.md).
## Communication
Please use [GitHub issues](https://github.com/DiffSharp/DiffSharp/issues) to share bug reports, feature requests, installation issues, suggestions etc.
## Contributing
We welcome all contributions.
* Bug fixes: if you encounter a bug, please open an [issue](https://github.com/DiffSharp/DiffSharp/issues) describing the bug. If you are planning to contribute a bug fix, please feel free to do so in a pull request.
* New features: if you plan to contribute new features, please first open an [issue](https://github.com/DiffSharp/DiffSharp/issues) to discuss the feature before creating a pull request.
## The Team
DiffSharp is developed by [Atılım Güneş Baydin](http://www.robots.ox.ac.uk/~gunes/), [Don Syme](https://www.microsoft.com/en-us/research/people/dsyme/) and other contributors, having started as a project supervised by the automatic differentiation wizards [Barak Pearlmutter](https://scholar.google.com/citations?user=AxFrw0sAAAAJ&hl=en) and [Jeffrey Siskind](https://scholar.google.com/citations?user=CgSBtPYAAAAJ&hl=en).
## License
DiffSharp is licensed under the BSD 2-Clause "Simplified" License, which you can find in the [LICENSE](https://github.com/DiffSharp/DiffSharp/blob/dev/LICENSE) file in this repository.