10

8

6

4

2


9.5

7.0

8.4

8.8

7.5
1.4

7.1
1.0

6.9

2.0

5.9

8.1

23 Machine Learning packages and projects

  • GoLearn

    9.5 7.0 Go
    General Machine Learning library for Go.
  • Gorgonia

    8.4 8.8 Go
    Gorgonia is a library that helps facilitate machine learning in Go.
  • goml

    7.5 1.4 Go
    On-line Machine Learning in Go
  • CloudForest

    7.1 1.0 Go
    Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go.
  • bayesian

    6.9 2.0 Go
    Naive Bayesian Classification for Golang.
  • gago

    5.9 8.1 Go
    Multi-population, flexible, parallel genetic algorithm.
  • gobrain

    5.4 0.9 Go
    Neural Networks written in go
  • go-galib

    5.2 0.0 Go
    Genetic Algorithms library written in Go / golang
  • regommend

    4.8 1.1 Go
    Recommendation & collaborative filtering engine
  • shield

    4.4 0.0 Go
    Bayesian text classifier with flexible tokenizers and storage backends for Go
  • go-fann

    4.2 0.0 Go
    Go bindings for Fast Artificial Neural Networks(FANN) library.
  • goRecommend

    3.8 0.0 Go
    Recommendation Algorithms library written in Go.
  • NEUGO

    3.7 5.1 Go
    NEUGO: Neural Networks in Go
  • libsvm

    3.6 0.0 Go
    libsvm golang version derived work based on LIBSVM 3.14.
  • goga

    3.4 0.0 Go
    Genetic algorithm library for Go.
  • neural-go

    3.3 0.0 Go
    A multilayer perceptron network implemented in Go, with training via backpropagation.
  • go-pr

    3.1 0.0 Go
    Pattern recognition package in Go lang.
  • EAGO

    3.0 6.6 Go
    EAGO: Evolutionary Algorithms in Go
  • golinear

    2.7 0.5 Go
    liblinear bindings for Go
  • neat

    2.6 8.6 Go
    Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT).
  • godist

    1.8 0.0 Go
    Various probability distributions, and associated methods.
  • probab

    0.8 0.0 Go
    Probability distribution functions. Bayesian inference. Written in pure Go.
  • mlgo

    0.6 0.0 Go
    This project aims to provide minimalistic machine learning algorithms in Go.

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