DiffSharp 0.5.0

.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There is a newer version of this package available.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 0.5.0
                    


                    

                
NuGet\Install-Package DiffSharp -Version 0.5.0
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="0.5.0" />
For projects that support PackageReference, copy this XML node into the project file to reference the package. 
<PackageVersion Include="DiffSharp" Version="0.5.0" />
                    


                            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 0.5.0
                    


                    

                
#r "nuget: DiffSharp, 0.5.0"
#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. 
#:package DiffSharp@0.5.0
#:package directive can be used in C# file-based apps starting in .NET 10 preview 4. Copy this into a .cs file before any lines of code to reference the package. 
#addin nuget:?package=DiffSharp&version=0.5.0
                    


                            Install as a Cake Addin
                        

                    

                
#tool nuget:?package=DiffSharp&version=0.5.0
                    


                            Install as a Cake Tool

DiffSharp (∂#) is an automatic differentiation (AD) library implemented in the F# language.

AD allows exact and efficient calculation of derivatives, by systematically applying the chain rule of calculus at the elementary operator level. AD is different from numerical differentiation, which is prone to truncation and round-off errors, and symbolic differentiation, which is exact but not efficient for run-time calculations and can only handle closed-form mathematical expressions.

Using the DiffSharp library, derivative calculations can be incorporated with minimal change into existing algorithms.

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 2,316 8/24/2019 0.8.4-beta is deprecated because it is no longer maintained.
0.8.3-beta 737 7/4/2019
0.8.2-beta 714 6/25/2019
0.8.1-beta 684 6/20/2019
0.8.0-beta 708 6/11/2019
0.7.7 5,455 12/25/2015
0.7.6 1,794 12/15/2015
0.7.5 1,876 12/6/2015
0.7.4 1,825 10/13/2015
0.7.3 1,870 10/6/2015
0.7.2 1,925 10/4/2015
0.7.1 1,737 10/4/2015
0.7.0 1,639 9/29/2015
0.6.3 2,104 7/18/2015
0.6.2 1,506 6/6/2015
0.6.1 1,542 6/2/2015
0.6.0 1,737 4/26/2015
0.5.10 1,556 3/27/2015
0.5.9 1,772 2/26/2015
0.5.8 1,931 2/23/2015
0.5.7 1,715 2/17/2015
0.5.6 1,730 2/13/2015
0.5.5 1,713 12/15/2014
0.5.4 1,783 11/23/2014
0.5.3 2,568 11/7/2014
0.5.2 2,253 11/4/2014
0.5.1 1,494 10/27/2014
0.5.0 1,558 10/2/2014