goRecommend VS golinear

Compare goRecommend vs golinear and see what are their differences.

goRecommend

Collaborative Filtering (CF) Algorithms in Go! (by timkaye11)

golinear

liblinear bindings for Go (by danieldk)
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goRecommend golinear
- -
203 44
- -
0.0 0.0
over 9 years ago over 5 years ago
Go Go
MIT License GNU General Public License v3.0 or later
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.

goRecommend

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

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

golinear

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

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

What are some alternatives?

When comparing goRecommend and golinear you can also consider the following projects:

go-pr - Pattern recognition package in Go lang.

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

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

goml - On-line Machine Learning in Go (and so much more)

gobrain - Neural Networks written in go

GoLearn - Machine Learning for Go

Varis - Golang Neural Network

bayesian - Naive Bayesian Classification for Golang.

godist - Probability distributions and associated methods in Go

go-galib - Genetic Algorithms library written 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