Machine Learning Packages
-
SFA.jl0Slow Feature Analysis in Julia
-
KaggleDigitRecognizer.jl0Julia code for Kaggle's Digit Recognizer competition
-
MochaTheano.jl0Allow use of Theano for automatic differentiation within Mocha, via PyCall
-
FeatureSelection.jl1Repository housing feature selection algorithms for use with the machine learning toolbox MLJ.
-
ConfidenceWeighted.jl1Confidence weighted classifier
-
Contingency.jl1Experimental automated machine learning for Julia.
-
Flimsy.jl1Gradient based Machine Learning for Julia
-
EGR.jl1-
-
SimpleML.jl1Textbook implementations of some Machine Learning Algorithms in Julia.
-
DAI.jl2A julia binding to the C++ discrete approximate inference library for graphical models: libDAI
-
Learn.jl2Base framework library for machine learning packages.
-
EmpiricalRiskMinimization.jl3Empirical Risk Minimization in Julia.
-
DecisionTrees.jl3-
-
NetworkLearning.jl3Baseline collective classification library
-
TheDataMustFlow.jl3Julia tools for feeding tabular data into machine learning.
-
HSIC.jl5Julia implementations of the Hilbert-Schmidt Independence Criterion (HSIC)
-
BNMF.jl5Bayesian Non-negative Matrix Factorization
-
SVMLightLoader.jl5Loader of svmlight / liblinear format files
-
FunctionalDataUtils.jl7Utility functions for the FunctionalData package, mainly from the area of computer vision / machine learning
-
Ollam.jl7OLLAM: Online Learning of Linear Adaptable Models
-
SoftConfidenceWeighted.jl8Exact Soft Confidence-Weighted Learning
-
ScikitLearnBase.jl9Abstract interface of ScikitLearn.jl
-
LIBLINEAR.jl12LIBLINEAR bindings for Julia
-
HopfieldNets.jl14Hopfield networks in Julia
-
ProjectiveDictionaryPairLearning.jl16Julia code for the paper S. Gu, L. Zhang, W. Zuo, and X. Feng, “Projective Dictionary Pair Learning for Pattern Classification,” In NIPS 2014
-
RegERMs.jl16DEPRECATED: Regularised Empirical Risk Minimisation Framework (SVMs, LogReg, Linear Regression) in Julia
-
LearnBase.jl17Abstractions for Julia Machine Learning Packages
-
PredictMD.jl17Uniform interface for machine learning in Julia
-
Ladder.jl17A reliable leaderboard algorithm for machine learning competitions
-
Discretizers.jl18A Julia package for data discretization and label maps
-
Keras.jl20Run keras models with a Flux backend
-
GradientBoost.jl22Gradient boosting framework for Julia.
-
KDTrees.jl25KDTrees for julia
-
ELM.jl27Extreme Learning Machine in julia
-
LearningStrategies.jl28A generic and modular framework for building custom iterative algorithms in Julia
-
ValueHistories.jl29Utilities to efficiently track learning curves or other optimization information
-
BayesianNonparametrics.jl31BayesianNonparametrics in julia
-
MLLabelUtils.jl32Utility package for working with classification targets and label-encodings
-
Strada.jl33A deep learning library for Julia based on Caffe
-
Mitosis.jl34Automatic probabilistic programming for scientific machine learning and dynamical models
Loading more...