GoLearn
General Machine Learning library for Go.
General Machine Learning library for Go.
graph-based computational library like Theano for Go that provides primitives for building various machine learning and neural network algorithms.
Go package for OCR (Optical Character Recognition), by using Tesseract C++ library.
A CLI tool to transpile trained classic ML models into a native Go code with zero dependencies, written in Python with Go language support.
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.
On-line Machine Learning in Go.
An accelerated Machine Learning framework for Go.
An evolutionary optimization library.
Go Interface to Open Neural Network Exchange (ONNX).
Naive Bayesian Classification for Golang.
A simple OCR API server, seriously easy to be deployed by Docker and Heroku.
Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go.
Neural Networks written in go.
A feature-rich neural network library in Go.
Huggingface transformer pipelines for golang with onnxruntime.
Recommendation & collaborative filtering engine.
Bayesian optimization framework for black-box functions written in Go. Everything will be optimized.
Genetic algorithm library for Go.
Recommendation Algorithms library written in Go.
Genetic Algorithms library written in Go / golang.
Bayesian text classifier with flexible tokenizers and storage backends for Go.
Go bindings for Fast Artificial Neural Networks(FANN) library.
Go Scoring API for PMML.
A simplistic Neural Network Library in Go.
A Deep Neural Network library written in Go.
Neural Network for Go.
Multilayer perceptron network implemented in Go, with training via backpropagation.
libsvm golang version derived work based on LIBSVM 3.14.
Pattern recognition package in Go lang.
Easy to use Random Forest library for Go.
Golang Neural Network.
Go implementation of the k-modes and k-prototypes clustering algorithms.
Various probability distributions, and associated methods.
Dynamic decision tree, create trees defining customizable rules.
Genetic Algorithm and Particle Swarm Optimization library.
Fast, scalable, high performance Gradient Boosting on Decision Trees library. Golang using Cgo for blazing fast inference CatBoost Model.
Probability distribution functions. Bayesian inference. Written in pure Go.
An offline recommender system backend based on collaborative filtering written in Go.