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https://github.com/promacanthus/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.
https://github.com/promacanthus/awesome-golang-ai
List: awesome-golang-ai
deep-learning golang machine-learning neural-network
Last synced: 23 days ago
JSON representation
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.
- Host: GitHub
- URL: https://github.com/promacanthus/awesome-golang-ai
- Owner: promacanthus
- License: mit
- Created: 2022-01-03T13:26:56.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-06-06T09:27:22.000Z (over 1 year ago)
- Last Synced: 2024-04-11T17:26:32.704Z (8 months ago)
- Topics: deep-learning, golang, machine-learning, neural-network
- Homepage: https://promacanthus.github.io/awesome-golang-ai/
- Size: 13.7 KB
- Stars: 5
- Watchers: 3
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - 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. (Programming Language Lists / Go Lists)
README
# 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](https://github.com/cdipaolo/goml):On-line Machine Learning in Go (and so much more).
- [golearn](https://github.com/sjwhitworth/golearn): simple and customizable batteries included ML library in Go.
- [gonum](https://github.com/gonum/gonum):Gonum is a set of numeric libraries for the Go programming language. It contains libraries for matrices, statistics, optimization, and more.
- [gorgonia](https://github.com/gorgonia/gorgonia): Gorgonia is a library that helps facilitate machine learning in Go.
- [spago](https://github.com/nlpodyssey/spago): Self-contained Machine Learning and Natural Language Processing library in Go.
- [goro](https://github.com/aunum/goro): A High-level Machine Learning Library for Go.
- [goga](https://github.com/tomcraven/goga): Golang Genetic Algorithm.
- [hep](https://github.com/go-hep/hep): hep is the mono repository holding all of go-hep.org/x/hep packages and tools.
- [hector](https://github.com/xlvector/hector): Golang machine learning lib.
- [sklearn](https://github.com/pa-m/sklearn): bits of sklearn ported to Go.## Neural Networks
- [gobrain](https://github.com/goml/gobrain): Neural Networks written in go.
- [go-neural](https://github.com/NOX73/go-neural): Neural network implementation on golang.
- [go-deep](https://github.com/patrikeh/go-deep): Artificial Neural Network.
- [olivia](https://github.com/olivia-ai/olivia): Your new best friend powered by an artificial neural network.
- [gomid](https://github.com/surenderthakran/gomind): A simplistic Neural Network Library in Go.
- [neurgo](https://github.com/tleyden/neurgo): Neural Network toolkit in Go.
- [gonn](https://github.com/fxsjy/gonn): GoNN is an implementation of Neural Network in Go Language, which includes BPNN, RBF, PCN.
- [gosom](https://github.com/milosgajdos/gosom): Self-organizing maps in Go.
- [go-perceptron-go](https://github.com/made2591/go-perceptron-go): A single / multi layer / recurrent neural network written in Golang.## Linear Algebra
- [gosl](https://github.com/cpmech/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](https://github.com/james-bowman/sparse): Sparse matrix formats for linear algebra supporting scientific and machine learning applications.## Probability Distributions
- [godist](https://github.com/e-dard/godist): Probability distributions and associated methods in Go.
## Decision Trees
- [CloudForest](https://github.com/ryanbressler/CloudForest)
## Regression- [regression](https://github.com/sajari/regression): Multivariable regression library in Go.
- [ridge](https://github.com/promacanthus/ridge): Ridge regression in Go.## Bayesian Classifiers
- [bayesian](https://github.com/jbrukh/bayesian): Naive Bayesian Classification for Golang.
- [multibayes](https://github.com/lytics/multibayes): Multiclass Naive Bayesian Classification.## Recommendation Engines
- [regommend](https://github.com/muesli/regommend): Recommendation engine for Go.
- [gorse](https://github.com/zhenghaoz/gorse): Go Recommender System Engine.
- [too](https://github.com/FurqanSoftware/too): Simple recommendation engine implementation built on top of Redis.## Evolutionary Algorithms
- [eaopt](https://github.com/MaxHalford/eaopt): Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution).
- [evo](https://github.com/cbarrick/evo): Evolutionary Algorithms in Go.## Graph
- [gogl](https://github.com/sdboyer/gogl): A graph library in Go.
## Cluster
- [gokmeans](https://github.com/mash/gokmeans): K-means algorithm implemented in Go (golang).
- [kmeans](https://github.com/muesli/kmeans): k-means clustering algorithm implementation written in Go.## Anomaly Detection
- [morgoth](https://github.com/nathanielc/morgoth): Metric anomaly detection.
- [anomalyzer](https://github.com/lytics/anomalyzer): Probabilistic anomaly detection for time series data.
- [goanomaly](https://github.com/sec51/goanomaly): Golang library for anomaly detection. Uses the Gaussian distribution and the probability density formula.## DataFrames
- [gota](https://github.com/go-gota/gota): Gota: DataFrames and data wrangling in Go.
- [dataframe-go](https://github.com/rocketlaunchr/dataframe-go): DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration.
- [qframe](https://github.com/tobgu/qframe): Immutable data frame for Go.## Explaining Model
- [lime](https://github.com/marcotcr/lime): Lime: Explaining the predictions of any machine learning classifier.
# Books
- [Machine Learning With go](https://github.com/promacanthus/awesome-golang-ai/blob/main/books/Machine%20Learning%20with%20Go.pdf)
- [Machine-Learning-With-Go](https://github.com/promacanthus/Machine-Learning-With-Go): example code.
- [机器学习:Go语言实现](https://github.com/promacanthus/awesome-golang-ai/blob/main/books/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%20Go%E8%AF%AD%E8%A8%80%E5%AE%9E%E7%8E%B0.pdf)
- [GO语言机器学习实战](https://book.douban.com/subject/35037170/)# Basic Knowledge
## Reinforcement Learning
- [Hands-on Reinforcement Learning](https://hrl.boyuai.com/)
# Datasets
- [LendingClub]()