How to: Machine Learning
_Libraries for Machine Learning._

- [bayesian](https://github.com/jbrukh/bayesian) - Naive Bayesian Classification for Golang.
- [catboost-cgo](https://github.com/mirecl/catboost-cgo) - Fast, scalable, high performance Gradient Boosting on Decision Trees library. Golang using Cgo for blazing fast inference CatBoost Model.
- [CloudForest](https://github.com/ryanbressler/CloudForest) - Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go.
- [ddt](https://github.com/sgrodriguez/ddt) - Dynamic decision tree, create trees defining customizable rules.
- [eaopt](https://github.com/MaxHalford/eaopt) - An evolutionary optimization library.
- [evoli](https://github.com/khezen/evoli) - Genetic Algorithm and Particle Swarm Optimization library.
- [fonet](https://github.com/Fontinalis/fonet) - A Deep Neural Network library written in Go.
- [go-cluster](https://github.com/e-XpertSolutions/go-cluster) - Go implementation of the k-modes and k-prototypes clustering algorithms.
- [go-deep](https://github.com/patrikeh/go-deep) - A feature-rich neural network library in Go.
- [go-fann](https://github.com/white-pony/go-fann) - Go bindings for Fast Artificial Neural Networks(FANN) library.
- [go-galib](https://github.com/thoj/go-galib) - Genetic Algorithms library written in Go / golang.
- [go-pr](https://github.com/daviddengcn/go-pr) - Pattern recognition package in Go lang.
- [gobrain](https://github.com/goml/gobrain) - Neural Networks written in go.
- [godist](https://github.com/e-dard/godist) - Various probability distributions, and associated methods.
- [goga](https://github.com/tomcraven/goga) - Genetic algorithm library for Go.
- [GoLearn](https://github.com/sjwhitworth/golearn) - General Machine Learning library for Go.
- [GoMind](https://github.com/surenderthakran/gomind) - A simplistic Neural Network Library in Go.
- [goml](https://github.com/cdipaolo/goml) - On-line Machine Learning in Go.
- [GoMLX](https://github.com/gomlx/gomlx) - An accelerated Machine Learning framework for Go.
- [gonet](https://github.com/dathoangnd/gonet) - Neural Network for Go.
- [Goptuna](https://github.com/c-bata/goptuna) - Bayesian optimization framework for black-box functions written in Go. Everything will be optimized.
- [goRecommend](https://github.com/timkaye11/goRecommend) - Recommendation Algorithms library written in Go.
- [gorgonia](https://github.com/gorgonia/gorgonia) - graph-based computational library like Theano for Go that provides primitives for building various machine learning and neural network algorithms.
- [gorse](https://github.com/zhenghaoz/gorse) - An offline recommender system backend based on collaborative filtering written in Go.
- [goscore](https://github.com/asafschers/goscore) - Go Scoring API for PMML.
- [gosseract](https://github.com/otiai10/gosseract) - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library.
- [hugot](https://github.com/knights-analytics/hugot) - Huggingface transformer pipelines for golang with onnxruntime.
- [libsvm](https://github.com/datastream/libsvm) - libsvm golang version derived work based on LIBSVM 3.14.
- [m2cgen](https://github.com/BayesWitnesses/m2cgen) - A CLI tool to transpile trained classic ML models into a native Go code with zero dependencies, written in Python with Go language support.
- [neural-go](https://github.com/schuyler/neural-go) - Multilayer perceptron network implemented in Go, with training via backpropagation.
- [ocrserver](https://github.com/otiai10/ocrserver) - A simple OCR API server, seriously easy to be deployed by Docker and Heroku.
- [onnx-go](https://github.com/owulveryck/onnx-go) - Go Interface to Open Neural Network Exchange (ONNX).
- [probab](https://github.com/ThePaw/probab) - Probability distribution functions. Bayesian inference. Written in pure Go.
- [randomforest](https://github.com/malaschitz/randomForest) - Easy to use Random Forest library for Go.
- [regommend](https://github.com/muesli/regommend) - Recommendation & collaborative filtering engine.
- [shield](https://github.com/eaigner/shield) - Bayesian text classifier with flexible tokenizers and storage backends for Go.
- [tfgo](https://github.com/galeone/tfgo) - Easy to use Tensorflow bindings: simplifies the usage of the official Tensorflow Go bindings. Define computational graphs in Go, load and execute models trained in Python.
- [Varis](https://github.com/Xamber/Varis) - Golang Neural Network.

**[⬆ back to top](#contents)**