Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/sethjuarez/numl

Machine Learning for .NET
https://github.com/sethjuarez/numl

Last synced: 18 days ago
JSON representation

Machine Learning for .NET

Host: GitHub
URL: https://github.com/sethjuarez/numl
Owner: sethjuarez
License: mit
Created: 2012-02-21T17:43:32.000Z (about 12 years ago)
Default Branch: master
Last Pushed: 2018-11-10T04:19:32.000Z (over 5 years ago)
Last Synced: 2024-02-02T19:07:35.787Z (4 months ago)
Language: C#
Homepage: http://numl.net
Size: 36.1 MB
Stars: 427
Watchers: 77
Forks: 106
Open Issues: 14
Metadata Files:
- Readme: README.md
- License: LICENSE.md

Lists

awesome-dotnet - numl - Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models. (Machine Learning and Data Science)
awsome-dotnet - numl - Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models. (Machine Learning and Data Science)
awesome-csharp - numl - Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models. (Machine Learning and Data Science)
awesome-dotnet - numl - Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models. (Machine Learning and Data Science)
awesome-dot-dev - numl - Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models. (Machine Learning and Data Science)
awesome-dotnet - numl - Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models. (Machine Learning and Data Science)
awesome-dotnet-cn - 官网
awesome-dotnet - numl - Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models. (Machine Learning and Data Science)
awesome-dotnet - numl - Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models. (Machine Learning and Data Science)
awesome-dotnet - numl - Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models. (Machine Learning and Data Science)
system-architecture-awesome - numl - Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models. (Machine Learning and Data Science)
awesome-dotnet-cn - numl - 包含最流行的监督学习和无监督学习算法，尽量减少创建预测模型时的冲突。 (机器学习和数据科学)
awesome-dotnet - numl - Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models. (Machine Learning and Data Science)
awesome-dotnet - numl - Designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved with creating the predictive models. (Machine Learning and Data Science)
awesome-dotnet-cn - 官网

README

        # Project Description

[![Join the chat at https://gitter.im/sethjuarez/numl](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/sethjuarez/numl?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

This library is designed to assist in the use of common Machine Learning Algorithms in conjunction with the .NET platform. 

It is designed to include the most popular supervised and unsupervised learning algorithms while minimizing the friction involved 

with creating the predictive models.

# Contributing

I would love to take contributions! Please read [this](https://guides.github.com/activities/contributing-to-open-source/). 

# Learn More

More details can be found at the project website: [http://numl.net](http://numl.net "numl.net").