Catalyst VS numl

Compare Catalyst vs numl and see what are their differences.

Catalyst

🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models. (by curiosity-ai)
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Catalyst numl
1 -
679 429
1.6% -
5.5 0.0
20 days ago over 5 years ago
C# C#
MIT License MIT 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.

Catalyst

Posts with mentions or reviews of Catalyst. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-12.

numl

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

We haven't tracked posts mentioning numl yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing Catalyst and numl you can also consider the following projects:

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

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

encog-dotnet-core

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.