DiffSharp 0.8.4-beta

.NET Standard 2.0
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This is a prerelease version of DiffSharp.
Install-Package DiffSharp -Version 0.8.4-beta
dotnet add package DiffSharp --version 0.8.4-beta
<PackageReference Include="DiffSharp" Version="0.8.4-beta" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add DiffSharp --version 0.8.4-beta
The NuGet Team does not provide support for this client. Please contact its maintainers for support.
#r "nuget: DiffSharp, 0.8.4-beta"
#r directive can be used in F# Interactive, C# scripting and .NET Interactive. Copy this into the interactive tool or source code of the script to reference the package.
// Install DiffSharp as a Cake Addin
#addin nuget:?package=DiffSharp&version=0.8.4-beta&prerelease

// Install DiffSharp as a Cake Tool
#tool nuget:?package=DiffSharp&version=0.8.4-beta&prerelease
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

DiffSharp is an automatic differentiation (AD) library.

AD allows exact and efficient calculation of derivatives, by systematically invoking the chain rule of calculus at the elementary operator level during program execution. AD is different from numerical differentiation, which is prone to truncation and round-off errors, and symbolic differentiation, which is affected by expression swell and cannot fully handle algorithmic control flow.

Using the DiffSharp library, derivative calculations (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products) can be incorporated with minimal change into existing algorithms. Diffsharp supports nested forward and reverse AD up to any level, meaning that you can compute exact higher-order derivatives or differentiate functions that are internally making use of differentiation. Please see the API Overview page for a list of available operations.

The library is under active development by Atılım Güneş Baydin and Barak A. Pearlmutter mainly for research applications in machine learning, as part of their work at the Brain and Computation Lab, Hamilton Institute, National University of Ireland Maynooth.

DiffSharp is implemented in the F# language and can be used from C# and the other languages running on .NET Core, Mono, or the .NET Framework; targeting the 64 bit platform. It is tested on Linux and Windows. We are working on interfaces/ports to other languages.

Product Versions
.NET net5.0 net5.0-windows net6.0 net6.0-android net6.0-ios net6.0-maccatalyst net6.0-macos net6.0-tvos net6.0-windows
.NET Core netcoreapp2.0 netcoreapp2.1 netcoreapp2.2 netcoreapp3.0 netcoreapp3.1
.NET Standard netstandard2.0 netstandard2.1
.NET Framework net461 net462 net463 net47 net471 net472 net48
MonoAndroid monoandroid
MonoMac monomac
MonoTouch monotouch
Tizen tizen40 tizen60
Xamarin.iOS xamarinios
Xamarin.Mac xamarinmac
Xamarin.TVOS xamarintvos
Xamarin.WatchOS xamarinwatchos
Compatible target framework(s)
Additional computed target framework(s)
Learn more about Target Frameworks and .NET Standard.

NuGet packages (1)

Showing the top 1 NuGet packages that depend on DiffSharp:

Package Downloads

Hype is a proof-of-concept deep learning library, where you can perform optimization on compositional machine learning systems of many components, even when such components themselves internally perform optimization. This is enabled by nested automatic differentiation (AD) giving you access to the automatic exact derivative of any floating-point value in your code with respect to any other. Underlying computations are run by a BLAS/LAPACK backend (OpenBLAS by default).

GitHub repositories

This package is not used by any popular GitHub repositories.

Version Downloads Last updated
0.8.4-beta 1,090 8/24/2019
0.8.3-beta 446 7/4/2019
0.8.2-beta 430 6/25/2019
0.8.1-beta 417 6/20/2019
0.8.0-beta 437 6/11/2019
0.7.7 3,994 12/25/2015
0.7.6 1,090 12/15/2015
0.7.5 1,140 12/6/2015
0.7.4 1,111 10/13/2015
0.7.3 1,290 10/6/2015
0.7.2 1,286 10/4/2015
0.7.1 1,127 10/4/2015
0.7.0 1,053 9/29/2015
0.6.3 1,558 7/18/2015
0.6.2 939 6/6/2015
0.6.1 967 6/2/2015
0.6.0 1,154 4/26/2015
0.5.10 977 3/27/2015
0.5.9 1,193 2/26/2015
0.5.8 1,353 2/23/2015
0.5.7 1,138 2/17/2015
0.5.6 1,150 2/13/2015
0.5.5 1,149 12/15/2014
0.5.4 1,193 11/23/2014
0.5.3 1,805 11/7/2014
0.5.2 1,687 11/4/2014
0.5.1 946 10/27/2014
0.5.0 961 10/2/2014

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