AForge.NET VS encog-dotnet-core

Compare AForge.NET vs encog-dotnet-core and see what are their differences.

AForge.NET

AForge.NET Framework is a C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence - image processing, neural networks, genetic algorithms, machine learning, robotics, etc. (by andrewkirillov)
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AForge.NET encog-dotnet-core
1 -
1,050 429
- -
0.0 0.0
3 months ago over 5 years ago
C# C#
GNU General Public License v3.0 or later GNU General Public License v3.0 or later
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.

AForge.NET

Posts with mentions or reviews of AForge.NET. We have used some of these posts to build our list of alternatives and similar projects.

encog-dotnet-core

Posts with mentions or reviews of encog-dotnet-core. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning encog-dotnet-core yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing AForge.NET and encog-dotnet-core you can also consider the following projects:

Accord.NET

Accord.NET Extensions

TensorFlow.NET - .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.

ML.NET - ML.NET is an open source and cross-platform machine learning framework for .NET.

Deedle - Easy to use .NET library for data and time series manipulation and for scientific programming

FsLab - FsLab project templates - download as ZIP to get started!

F# Data - F# Data: Library for Data Access

Spreads - Series and Panels for Real-time and Exploratory Analysis of Data Streams