semasaf/awesome-golang-ai

Golang AI applications have incredible potential. With unique features like inexplicable speed, easy debugging, concurrency, and excellent libraries for ML, deep learning, and reinforcement learning.

Awesome Golang.ai

Golang AI applications have incredible potential. With unique features like inexplicable speed, easy debugging, concurrency, and excellent libraries for ML, deep learning, and reinforcement learning.

General Machine Learning libraries

• goml：On-line Machine Learning in Go (and so much more).
• golearn: simple and customizable batteries included ML library in Go.
• gonum：Gonum is a set of numeric libraries for the Go programming language. It contains libraries for matrices, statistics, optimization, and more.
• gorgonia: Gorgonia is a library that helps facilitate machine learning in Go.
• spago: Self-contained Machine Learning and Natural Language Processing library in Go.
• goro: A High-level Machine Learning Library for Go.
• goga: Golang Genetic Algorithm.
• hep: hep is the mono repository holding all of go-hep.org/x/hep packages and tools.
• hector: Golang machine learning lib.
• sklearn: bits of sklearn ported to Go.

Neural Networks

• gobrain: Neural Networks written in go.
• go-neural: Neural network implementation on golang.
• go-deep: Artificial Neural Network.
• olivia: Your new best friend powered by an artificial neural network.
• gomid: A simplistic Neural Network Library in Go.
• neurgo: Neural Network toolkit in Go.
• gonn: GoNN is an implementation of Neural Network in Go Language, which includes BPNN, RBF, PCN.
• gosom: Self-organizing maps in Go.
• go-perceptron-go: A single / multi layer / recurrent neural network written in Golang.

Linear Algebra

• gosl: Linear algebra, eigenvalues, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, differential equations.
• sparse: Sparse matrix formats for linear algebra supporting scientific and machine learning applications.

Probability Distributions

• godist: Probability distributions and associated methods in Go.

Decision Trees

• CloudForest

Regression

• regression: Multivariable regression library in Go.
• ridge: Ridge regression in Go.

Bayesian Classifiers

• bayesian: Naive Bayesian Classification for Golang.
• multibayes: Multiclass Naive Bayesian Classification.

Recommendation Engines

• regommend: Recommendation engine for Go.
• gorse: Go Recommender System Engine.
• too: Simple recommendation engine implementation built on top of Redis.

Evolutionary Algorithms

• eaopt: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution).
• evo: Evolutionary Algorithms in Go.

Graph

• gogl: A graph library in Go.

Cluster

• gokmeans: K-means algorithm implemented in Go (golang).
• kmeans: k-means clustering algorithm implementation written in Go.

Anomaly Detection

• morgoth: Metric anomaly detection.
• anomalyzer: Probabilistic anomaly detection for time series data.
• goanomaly: Golang library for anomaly detection. Uses the Gaussian distribution and the probability density formula.

DataFrames

• gota: Gota: DataFrames and data wrangling in Go.
• dataframe-go: DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration.
• qframe: Immutable data frame for Go.

Explaining Model

• lime: Lime: Explaining the predictions of any machine learning classifier.

Books

• Machine Learning With go
• Machine-Learning-With-Go: example code.
• 机器学习：Go语言实现
• GO语言机器学习实战

Basic Knowledge

Reinforcement Learning

• Hands-on Reinforcement Learning

Datasets

• LendingClub