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Accord.NET

Accord.NET

A machine learning framework for scientific computing, composed of multiple libraries to cover wide range applications.

Accord.NET is a .NET machine learning framework. It is combined with scientific computing, audio and image processing libraries. It is written in C# programming language. It is built to extend the capabilities and features of AForge.NET. Later merged together as Accord.NET.

It is a comprehensive framework for creating production-grade computer vision, computer audition, signal processing, machine learning, mathematics, statistics and other computing techniques. It can also be used commercially.

Accord.NET's libraries are:

Scientific Computing libraries

Accord.Math

Accord.Statistics

Accord.MachineLearning

Accord.Neuro

Signal and Image Processing libraries

Accord.Imaging

Accord.Audio

Accord.Vision

Support Libraries

Accord.Controls

Accord.Controls.Imaging

Accord.Controls.Audio

Accord.Controls.Vision

Accord.NET is applicable on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

Ceazar Roberto de Souza created Accord.NET and developing it along with other collaborators.

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Further reading

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A Tutorial on Principal Component Analysis with the Accord.NET Framework

César Roberto de Souza

Academic paper

Procedural Generation of Videos to Train Deep Action Recognition Networks

César Roberto de Souza, Adrien Gaidon, Yohann Cabon and Antonio Manuel López Peña

Academic paper

Documentaries, videos and podcasts

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