Machine Learning Packages

AbstractGPs.jl192Abstract types and methods for Gaussian Processes.

AdvancedPS.jl50Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms

ApproxBayes.jl48Approximate Bayesian Computation (ABC) algorithms for likelihood free inference in julia

AugmentedGaussianProcesses.jl132Gaussian Process package based on data augmentation, sparsity and natural gradients

AutoMLPipeline.jl325A package that makes it trivial to create and evaluate machine learning pipeline architectures.

Avalon.jl105Starter kit for legendary models

BackpropNeuralNet.jl47A neural network in Julia

BayesianNonparametrics.jl30BayesianNonparametrics in julia

BayesianOptimization.jl77Bayesian optimization for Julia

BetaML.jl72Beta Machine Learning Toolkit

BNMF.jl4Bayesian Nonnegative Matrix Factorization

BoltzmannMachines.jl38A Julia package for training and evaluating multimodal deep Boltzmann machines

BrainFlow.jl935BrainFlow is a library intended to obtain, parse and analyze EEG, EMG, ECG and other kinds of data from biosensors

CartesianGeneticProgramming.jl69Cartesian Genetic Programming for Julia

Clustering.jl311A Julia package for data clustering

CombineML.jl41Create ensembles of machine learning models from scikitlearn, caret, and julia

ConfidenceWeighted.jl1Confidence weighted classifier

ConformalPrediction.jl58Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.

Contingency.jl1Experimental automated machine learning for Julia.

CounterfactualExplanations.jl75A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

DAI.jl2A julia binding to the C++ discrete approximate inference library for graphical models: libDAI

DataAugmentation.jl37Flexible data augmentation library for machine and deep learning

DataLoaders.jl71A parallel iterator for large machine learning datasets that don't fit into memory inspired by PyTorch's `DataLoader` class.

DecisionTree.jl316Julia implementation of Decision Tree (CART) and Random Forest algorithms

DecisionTrees.jl3

DiffEqFlux.jl771Universal neural differential equations with O(1) backprop, GPUs, and stiff+nonstiff DE solvers, demonstrating scientific machine learning (SciML) and physicsinformed machine learning methods

Discretizers.jl18A Julia package for data discretization and label maps

Dojo.jl221A differentiable physics engine for robotics

EasyML.jl52A foolproof way of doing ML with GUI elements.

EGR.jl1

ELM.jl27Extreme Learning Machine in julia

Embeddings.jl73Functions and data dependencies for loading various word embeddings (Word2Vec, FastText, GLoVE)

EmpiricalRiskMinimization.jl3Empirical Risk Minimization in Julia.

Enzyme.jl311Julia bindings for the Enzyme automatic differentiator

EvoTrees.jl143Boosted trees in Julia

ExplainableAI.jl77XAI in Julia using Flux.

FastAI.jl557Repository of best practices for deep learning in Julia, inspired by fastai

FeatureSelection.jl1Common measures and algorithms for feature selection

Flimsy.jl1Gradient based Machine Learning for Julia

FluxJS.jl43I heard you like compile times
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