Machine Learning Go Libraries Ranking

- 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.

- Huggingface transformer pipelines for golang with onnxruntime.

- Neural Networks written in go.

- A feature-rich neural network library in Go.

- 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.

- Deep learning framework inspired by Burn (Rust), with autograd, type-safe tensors, and zero-CGO GPU acceleration.

- 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.

- Machine learning compiler in Go, a from-scratch tinygrad port with a WebGPU backend.

- 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.

- Data engineering and classic ML toolkit with batch processing, type coercion, and 7 algorithms in pure Go with zero dependencies.