autodiff VS MKL.NET

Compare autodiff vs MKL.NET and see what are their differences.

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autodiff MKL.NET
7 3
1,534 149
1.1% 2.7%
7.8 6.7
about 1 month ago 5 months ago
C++ C#
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

autodiff

Posts with mentions or reviews of autodiff. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-22.

MKL.NET

Posts with mentions or reviews of MKL.NET. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing autodiff and MKL.NET you can also consider the following projects:

CppAD - A C++ Algorithmic Differentiation Package: Home Page

MathNet - Math.NET Numerics

FastAD - FastAD is a C++ implementation of automatic differentiation both forward and reverse mode.

Math3D - A .NET Standard 2.0 library for simple and efficient 3D math that is a feature-rich replacement for System.Numerics https://vimaec.github.io/Math3D

AngouriMath - New open-source cross-platform symbolic algebra library for C# and F#. Can be used for both production and research purposes.

CppRobotics - Header-only C++ library for robotics, control, and path planning algorithms. Work in progress, contributions are welcome!

Microsoft Automatic Graph Layout - A set of tools for graph layout and viewing

PythonRobotics - Python sample codes for robotics algorithms.

Rationals - 🔟 Implementation of rational number arithmetic for .NET with arbitrary precision.

AutoDiff - A .NET library that provides fast, accurate and automatic differentiation (computes derivative / gradient) of mathematical functions.