gago VS regommend

Compare gago vs regommend and see what are their differences.

gago

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

regommend

Recommendation engine for Go (by muesli)
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gago regommend
1 -
869 310
- -
0.0 0.0
about 1 year ago over 4 years ago
Go Go
MIT License GNU Affero General Public License v3.0
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.

gago

Posts with mentions or reviews of gago. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-18.

regommend

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

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

What are some alternatives?

When comparing gago and regommend you can also consider the following projects:

go-galib - Genetic Algorithms library written in Go / golang

GoLearn - Machine Learning for Go

goga - Golang Genetic Algorithm

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

go-pr - Pattern recognition package in Go lang.

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

CloudForest - Ensembles of decision trees in go/golang.

m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

neural-go - A multilayer perceptron network implemented in Go, with training via backpropagation.