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Accord.NET

Accord.NET

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

OverviewStructured DataIssuesContributors

Contents

accord-framework.net
Is a
Company
Company
Organization
Organization

Company attributes

Industry
Artificial Intelligence (AI)
Artificial Intelligence (AI)
Machine learning
Machine learning
Technology
Technology
Open-source software
Open-source software
Location
Auvergne-Rhône-Alpes
Auvergne-Rhône-Alpes
Grenoble
Grenoble
B2X
B2B
B2B
0
Number of Employees (Ranges)
51 – 2000
Founded Date
2008
0

Other attributes

Company Operating Status
Active
Source Code
github.com/accord-ne.../framework
Wikidata ID
Q25000538

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.

Timeline

No Timeline data yet.

Funding Rounds

Products

Acquisitions

SBIR/STTR Awards

Patents

Further Resources

Title
Author
Link
Type
Date

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

César Roberto de Souza

https://arxiv.org/ftp/arxiv/papers/1210/1210.7463.pdf

Academic paper

Accord.NET Framework

https://sourceforge.net/projects/accord-net/

Web

October 19, 2017

Procedural Generation of Videos to Train Deep Action Recognition Networks

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

https://arxiv.org/pdf/1612.00881.pdf

Academic paper

References

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