Machine Learning

This page summarizes the projects mentioned and recommended in the original post on /r/golang

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  • mab

    Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling and epsilon-greedy.

    Here's an example of multi-armed bandits done in Go: https://github.com/stitchfix/mab

  • mlgo

    Machine Learning with Go (golang) Session Material for GOLAB 2018 (by miku)

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  • GoLearn

    Machine Learning for Go

  • goml

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

  • spaGO

    Self-contained Machine Learning and Natural Language Processing library in Go

  • dataframe-go

    DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration

  • randomforest

    Random Forest implementation in golang

    I did end up writing and using a custom library for Random Forest (it's also in AwesomGo) in one real-world project (detecting Alzheimer's and Parkinson's from speech from a mobile app) - https://github.com/malaschitz/randomForest I had better results than the team who used TensorFlow and most importantly I didn't have to use any other technology than Go. For NN's it's probably best to use https://gorgonia.org/ - but it's not exactly a user friendly library. But there is a whole book on it - Hands-On Deep Learning with Go.

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  • Gorgonia

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

    I did end up writing and using a custom library for Random Forest (it's also in AwesomGo) in one real-world project (detecting Alzheimer's and Parkinson's from speech from a mobile app) - https://github.com/malaschitz/randomForest I had better results than the team who used TensorFlow and most importantly I didn't have to use any other technology than Go. For NN's it's probably best to use https://gorgonia.org/ - but it's not exactly a user friendly library. But there is a whole book on it - Hands-On Deep Learning with Go.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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