Machine Learning Libraries in Go Language

Go , an open source language by Google was initially created by  group of engineers who were frustrated with C++. Ever since their creation, the language has gotten traction for its simplicity. It ranked highly in the programming popularity indexes of Redmonk & TiOBE.

Some of the companies & use cases for Go Include:

  • Google, for many projects, notably including download server dl.google.com

  • Dropbox, migrated some of their critical components from Python to Go

  • CloudFlare, for their delta-coding proxy Railgun, their distributed DNS service, as well as tools for cryptography, logging, stream processing, and accessing SPDY sites

  • SoundCloud, for "dozens of systems"

  • The BBC, in some games and internal projects

  • Novartis, for an internal inventory system

  • Splice, for the entire backend (API and parsers) of their online music collaboration platform

  • Cloud Foundry, a platform as a service

  • CoreOS, a Linux-based operating system that utilizes Docker containers

  • MongoDB, tools for administrating MongoDB instances

  • Zerodha, for realtime peering and streaming of market data

  • Chango, a programmatic advertising company uses Go in its real-time bidding systems.

  • SendGrid, a Boulder, Colorado-based transactional email delivery and management service.[

  • Plug.dj, an interactive online social music streaming website.

Once a programming language starts to get traction, analytics libraries are not far behind.  We have collected a list of Machine Libraries in Go Language for people interested in experimenting with Go or already familiar with Go.

  1. Generalized Machine Learning Libraries:

    1. GoML - https://github.com/cdipaolo/goml

    2. Machine Learning libraries for Go Lang : https://github.com/alonsovidales/go_ml:

    3. MLGo - https://code.google.com/p/mlgo/

    4. GoLearn:

  2. Neural Networks

    1. Neural Networks written in go : https://github.com/goml/gobrain

    2. Go Fann - https://github.com/white-pony/go-fann

    3. Multi-Layer Perceptron Neural Network - https://github.com/schuyler/neural-go -

    4. Genetic Algorithms library written in Go / golang - https://github.com/thoj/go-galib

  3. Linear Algebra:

    1. Linear Algebra for Go & Matrix Library:

    2. Mat64: Package mat64 provides basic linear algebra operations for float64 matrices.

    3. BLAS Implementation for Go

    4. https://github.com/danieldk/golinear - liblinear bindings for Go

  4. Probability Distribution Functions

    1. http://godoc.org/code.google.com/p/probab

    2. https://github.com/e-dard/godist

  5. Decision Trees:

    1. Hector https://github.com/xlvector/hector

    2. Decision Trees in Go - https://github.com/ajtulloch/decisiontrees

    3. CloudForest - https://github.com/ryanbressler/CloudForest -

  6. Bayesian Classifiers:

    1. https://github.com/jbrukh/bayesian - Perform naive Bayesian classification

    2. https://github.com/eaigner/shield - Bayesian text classifier

  7. Recommendation Engines in Go

    1. Collaborative Filtering (CF) Algorithms in Go - https://github.com/timkaye11/goRecommend

    2. Recommendation engine for Go - https://github.com/muesli/regommend

A more detailed compilation is available on Fodop

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