DiffSharp 0.6.1

.NET Framework This package is compatible with all versions of .NET Framework.
There is a newer version of this package available.
See the version list below for details. 
dotnet add package DiffSharp --version 0.6.1
                    


                    

                
NuGet\Install-Package DiffSharp -Version 0.6.1
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="0.6.1" />
For projects that support PackageReference, copy this XML node into the project file to reference the package. 
<PackageVersion Include="DiffSharp" Version="0.6.1" />
                    


                            Directory.Packages.props
                        

                    

                
<PackageReference Include="DiffSharp" />
                    


                            Project file
For projects that support Central Package Management (CPM), copy this XML node into the solution Directory.Packages.props file to version the package. 
paket add DiffSharp --version 0.6.1
                    


                    

                
#r "nuget: DiffSharp, 0.6.1"
#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. 
#addin nuget:?package=DiffSharp&version=0.6.1
                    


                            Install as a Cake Addin
                        

                    

                
#tool nuget:?package=DiffSharp&version=0.6.1
                    


                            Install as a Cake Tool

DiffSharp is an automatic differentiation (AD) library implemented in the F# language. It supports C# and the other CLI languages.

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 suffers from expression swell and cannot 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. Operations can be nested to any level, meaning that you can compute exact higher-order derivatives and 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.

Product Compatible and additional computed target framework versions.
.NET Framework net is compatible. 
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

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 2,201 8/24/2019 0.8.4-beta is deprecated because it is no longer maintained.
0.8.3-beta 697 7/4/2019
0.8.2-beta 662 6/25/2019
0.8.1-beta 653 6/20/2019
0.8.0-beta 670 6/11/2019
0.7.7 5,366 12/25/2015
0.7.6 1,728 12/15/2015
0.7.5 1,812 12/6/2015
0.7.4 1,753 10/13/2015
0.7.3 1,799 10/6/2015
0.7.2 1,856 10/4/2015
0.7.1 1,669 10/4/2015
0.7.0 1,572 9/29/2015
0.6.3 2,040 7/18/2015
0.6.2 1,437 6/6/2015
0.6.1 1,476 6/2/2015
0.6.0 1,673 4/26/2015
0.5.10 1,491 3/27/2015
0.5.9 1,702 2/26/2015
0.5.8 1,865 2/23/2015
0.5.7 1,646 2/17/2015
0.5.6 1,662 2/13/2015
0.5.5 1,649 12/15/2014
0.5.4 1,715 11/23/2014
0.5.3 2,469 11/7/2014
0.5.2 2,185 11/4/2014
0.5.1 1,425 10/27/2014
0.5.0 1,488 10/2/2014

Please visit

https://github.com/gbaydin/DiffSharp/releases

for the latest release notes.