DiffSharp 0.7.7

There is a newer prerelease version of this package available.
See the version list below for details.
dotnet add package DiffSharp --version 0.7.7
NuGet\Install-Package DiffSharp -Version 0.7.7
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="DiffSharp" Version="0.7.7" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add DiffSharp --version 0.7.7
#r "nuget: DiffSharp, 0.7.7"
#r directive can be used in F# Interactive and Polyglot Notebooks. 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=0.7.7

// Install DiffSharp as a Cake Tool
#tool nuget:?package=DiffSharp&version=0.7.7

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 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 Compatible and additional computed target framework versions.
.NET Framework net46 is compatible.  net461 was computed.  net462 was computed.  net463 was computed.  net47 was computed.  net471 was computed.  net472 was computed.  net48 was computed.  net481 was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
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 1,639 8/24/2019
0.8.3-beta 565 7/4/2019
0.8.2-beta 542 6/25/2019
0.8.1-beta 526 6/20/2019
0.8.0-beta 553 6/11/2019
0.7.7 4,810 12/25/2015
0.7.6 1,457 12/15/2015
0.7.5 1,542 12/6/2015
0.7.4 1,484 10/13/2015
0.7.3 1,561 10/6/2015
0.7.2 1,596 10/4/2015
0.7.1 1,415 10/4/2015
0.7.0 1,320 9/29/2015
0.6.3 1,808 7/18/2015
0.6.2 1,213 6/6/2015
0.6.1 1,239 6/2/2015
0.6.0 1,432 4/26/2015
0.5.10 1,251 3/27/2015
0.5.9 1,466 2/26/2015
0.5.8 1,624 2/23/2015
0.5.7 1,397 2/17/2015
0.5.6 1,413 2/13/2015
0.5.5 1,422 12/15/2014
0.5.4 1,474 11/23/2014
0.5.3 2,121 11/7/2014
0.5.2 1,948 11/4/2014
0.5.1 1,199 10/27/2014
0.5.0 1,248 10/2/2014

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