MKL.NET VS autodiff

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

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MKL.NET autodiff
3 7
148 1,532
3.4% 2.5%
6.7 7.5
5 months ago 21 days ago
C# C++
Apache License 2.0 MIT License
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.

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.

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.

What are some alternatives?

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

MathNet - Math.NET Numerics

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

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

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

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

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

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

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

PythonRobotics - Python sample codes for robotics algorithms.

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