Accord.NET is a framework for scientific computing in .NET. The framework is comprised of multiple libraries encompassing a wide range of scientific computing applications, such as statistical data processing, machine learning, artificial intelligence, pattern recognition, including but not limited to, computer vision and computer audition. The framework offers a large number of probability distributions, hypothesis tests, kernel functions and support for most popular performance measurements techniques.
The framework comprises a set of libraries that are available in source code as well as via executable installers and NuGet packages. The main areas covered include numerical linear algebra, numerical optimization, statistics, machine learning, artificial neural networks, signal and image processing, and support libraries (such as graph plotting and visualization). The project was originally created to extend the capabilities of the AForge.NET Framework, but has since incorporated AForge.NET inside itself. Newer releases have united both frameworks under the Accord.NET name.
The Accord.NET Framework has been featured in multiple books such as Mastering.NET Machine Learning by PACKT publishing and F# for Machine Learning Applications, featured in QCON San Francisco, and currently accumulates more than 1,500 forks in GitHub.