Machine Learning

• mab

  2 46 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

• mlgo

  1 16 0.0 Makefile

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

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• GoLearn

  3 9,155 0.0 Go

  Machine Learning for Go

• goml

  3 1,537 0.0 Go

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

• spaGO

  11 1,693 0.0 Go

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

  

• dataframe-go

  3 1,112 0.0 Go

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

  

• randomforest

  2 39 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.

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• Gorgonia

  21 5,326 2.8 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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