Skip to content

Latest commit

 

History

History
55 lines (53 loc) · 6.83 KB

machine-learning.md

File metadata and controls

55 lines (53 loc) · 6.83 KB
Raw

Machine Learning

Tools that provide specific statistical algorithms which allow learning from data.

  • bayesian - Naive Bayesian Classification for Golang.
  • CloudForest - Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go.
  • gago - Multi-population, flexible, parallel genetic algorithm.
  • go-fann - Go bindings for Fast Artificial Neural Networks(FANN) library.
  • go-galib - Genetic Algorithms library written in Go / golang
  • go-pr - Pattern recognition package in Go lang.
  • gobrain - Neural Networks written in go
  • godist - Various probability distributions, and associated methods.
  • goga - Genetic algorithm library for Go.
  • GoLearn - General Machine Learning library for Go.
  • golinear - liblinear bindings for Go
  • goml - On-line Machine Learning in Go
  • goRecommend - Recommendation Algorithms library written in Go.
  • gorgonia - graph-based computational library like Theano for Go that provides primitives for building various machine learning and neural network algorithms.
  • libsvm - libsvm golang version derived work based on LIBSVM 3.14.
  • mlgo - This project aims to provide minimalistic machine learning algorithms in Go.
  • neural-go - A multilayer perceptron network implemented in Go, with training via backpropagation.
  • probab - Probability distribution functions. Bayesian inference. Written in pure Go.
  • regommend - Recommendation & collaborative filtering engine
  • shield - Bayesian text classifier with flexible tokenizers and storage backends for Go
  • PredictionIO Ruby SDK - The PredictionIO Ruby SDK provides a convenient API to quickly record your users' behavior and retrieve personalized predictions for them.
  • rb-libsvm - Ruby language bindings for LIBSVM. SVM is a machine learning and classification algorithm.
  • Ruby Datumbox Wrapper - It's a simple Ruby Datumbox Wrapper. At the moment the API currently allows you to build applications that make use of machine learning algorithms.
  • weka - Machine learning and data mining algorithms for JRuby.
  • Swift-AI - Highly optimized Artificial Intelligence and Machine Learning library written in Swift. 🔶
  • Swift-Brain - Artificial Intelligence/Machine Learning data structures and Swift algorithms for future iOS development. Bayes theorem, Neural Networks, and more AI. 🔶
  • AIToolbox - A toolbox of AI modules written in Swift: Graphs/Trees, Linear Regression, Support Vector Machines, Neural Networks, PCA, KMeans, Genetic Algorithms, MDP, Mixture of Gaussians. 🔶
  • Tensorflow-iOS - The official Google-built powerful neural network library port for iOS.
  • Pattern Recognition and Machine Learning - Christopher M. Bishop 2007
  • Neural Networks for Pattern Recognition - Christopher M. Bishop 1995
  • Probabilistic Graphical Models: Principles and Techniques - Daphne Koller and Nir Friedman 2009
  • Pattern Classification - Peter E. Hart, David G. Stork, and Richard O. Duda 2000
  • Machine Learning - Tom M. Mitchell 1997
  • Gaussian processes for machine learning - Carl Edward Rasmussen and Christopher K. I. Williams 2005
  • Neural Networks and Deep Learning - Michael Nielsen 2014
  • Bayesian Reasoning and Machine Learning - David Barber, Cambridge University Press, 2012
  • A Gentle Tutorial of the EM Algorithm - Jeff A. Bilmes (UC Berkeley) 1998
  • Introduction To Bayesian Inference - Christopher Bishop (Microsoft Research) 2009
  • Support Vector Machines - Chih-Jen Lin (National Taiwan University) 2006
  • Bayesian or Frequentist, Which Are You? - Michael I. Jordan (UC Berkeley)
  • Apache Flink - Fast and reliable large-scale data processing engine.
  • Apache Mahout - Scalable algorithms focused on collaborative filtering, clustering and classification.
  • Apache Spark - Data analytics cluster computing framework.
  • DatumBox - Provides several algorithms and pre-trained models for natural language processing.
  • DeepDive - Creates structured information from unstructured data and integrates it into an existing database.
  • Deeplearning4j - Distributed and multi-threaded deep learning library.
  • H2O - Analytics engine for statistics over big data.
  • JSAT - Algorithms for pre-processing, classification, regression, and clustering with support for multi-threaded execution.
  • Oryx 2 - A framework for building real-time large scale machine learning applications, which also includes end-to-end applications for collaborative filtering, classification, regression, and clustering.
  • Smile - The Statistical Machine Intelligence and Learning Engine provides a set of machine learning algorithms and a visualization library.
  • Weka - Collection of algorithms for data mining tasks ranging from pre-processing to visualization.