Accord.NET Framework Portable
3.8.0Use this .NET machine learning framework for advanced statistics, artificial intelligence applications computer vision or image processing
Accord.NET Framework Portable was developed in order to provide users with the means to perform machine learning, statistics, image processing or even artificial intelligence applications, all within a unified API that offers both machine training and machine learning.
Completely written in C#, together with AForge.NET Framework, it will allow one to perform operations such as classification, regression, clustering, distributions, hypothesis testing kernel methods, image and audio processing.
Users are advised to rely on NuGet for installing the framework when dealing with Visual Studio; if using Unity3D applications, the library DLL files will need to be copied in the plugins folder.
The main working principle behind Accord.NET Framework Portable is a sequence comprised of several steps, which include the selection of learning algorithms as the basis upon machine learning takes place, deriving machine learning models from the data and use the model methods such as Transform, Decide, Scores, Probabilities or LogLikelihoods.
For those who aren’t so well accustomed to such endeavors, or those who simply prefer a demonstration of the framework’s capabilities, it does come with a series of sample applications which are accessible in the corresponding, “Samples” folder. Furthermore, a detailed documentation is available online, for a better understanding.
Completely written in C#, together with AForge.NET Framework, it will allow one to perform operations such as classification, regression, clustering, distributions, hypothesis testing kernel methods, image and audio processing.
Users are advised to rely on NuGet for installing the framework when dealing with Visual Studio; if using Unity3D applications, the library DLL files will need to be copied in the plugins folder.
The main working principle behind Accord.NET Framework Portable is a sequence comprised of several steps, which include the selection of learning algorithms as the basis upon machine learning takes place, deriving machine learning models from the data and use the model methods such as Transform, Decide, Scores, Probabilities or LogLikelihoods.
For those who aren’t so well accustomed to such endeavors, or those who simply prefer a demonstration of the framework’s capabilities, it does come with a series of sample applications which are accessible in the corresponding, “Samples” folder. Furthermore, a detailed documentation is available online, for a better understanding.
System requirements
296 MB
Info
Update Date
Jun 11 2019
Version
3.8.0
License
LGPL
Created By
Cesar Roberto de Souza
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