Machine Learning

1. mab

   1 2 55 0.0 Go

   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

2. InfluxDB

   www.influxdata.com featured

   InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

   

3. mlgo

   2 1 18 4.6 Makefile

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

   

4. GoLearn

   3 3 9,373 0.0 Go

   Machine Learning for Go

   

5. goml

   4 3 1,584 0.0 Go

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

   

6. spaGO

   5 11 1,693 0.0 Go

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

   

7. dataframe-go

   6 3 1,229 0.0 Go

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

   

8. randomforest

   7 2 53 2.6 Go

   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.

9. SaaSHub

   www.saashub.com featured

   SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

   

10. Gorgonia

    8 22 5,788 1.1 Go

    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.

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