go-pr VS goml

Compare go-pr vs goml and see what are their differences.


Pattern recognition package in Go lang. (by daviddengcn)


On-line Machine Learning in Go (and so much more) (by cdipaolo)
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go-pr goml
0 1
61 1,344
- -
0.0 0.4
almost 9 years ago 7 months ago
Go Go
- 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.


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

We haven't tracked posts mentioning go-pr yet.
Tracking mentions began in Dec 2020.


Posts with mentions or reviews of goml. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-10-16.

What are some alternatives?

When comparing go-pr and goml you can also consider the following projects:

GoLearn - Machine Learning for Go

Gorgonia - Gorgonia is a library that helps facilitate machine learning in Go.

gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library

go-fann - Go bindings for FANN, library for artificial neural networks

Goptuna - A hyperparameter optimization framework, inspired by Optuna.

randomforest - Random Forest implementation in golang

sklearn - bits of sklearn ported to Go #golang

gobrain - Neural Networks written in go

golinear - liblinear bindings for Go

evoli - Genetic Algorithm and Particle Swarm Optimization

shield - Bayesian text classifier with flexible tokenizers and storage backends for Go

gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)