encog-dotnet-core VS Deedle

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

Deedle

Easy to use .NET library for data and time series manipulation and for scientific programming (by fslaborg)
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encog-dotnet-core Deedle
- 14
429 914
- 0.1%
0.0 0.0
over 5 years ago 10 months ago
C# F#
GNU General Public License v3.0 or later BSD 2-clause "Simplified" License
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.

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.

Deedle

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

What are some alternatives?

When comparing encog-dotnet-core and Deedle you can also consider the following projects:

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.

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

Accord.NET

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

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

Accord.NET Extensions

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

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