Abstract types and methods for Gaussian Processes.

Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms

Approximate Bayesian Computation (ABC) algorithms for likelihood free inference in julia

Gaussian Process package based on data augmentation, sparsity and natural gradients

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

Starter kit for legendary models

A neural network in Julia

BayesianNonparametrics in julia

Bayesian optimization for Julia

Beta Machine Learning Toolkit

Bayesian Non-negative Matrix Factorization

A Julia package for training and evaluating multimodal deep Boltzmann machines

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

Cartesian Genetic Programming for Julia

A Julia package for data clustering

Create ensembles of machine learning models from scikit-learn, caret, and julia

Confidence weighted classifier

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

Experimental automated machine learning for Julia.

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

A julia binding to the C++ discrete approximate inference library for graphical models: libDAI

Flexible data augmentation library for machine and deep learning

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

Julia implementation of Decision Tree (CART) and Random Forest algorithms

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Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

A Julia package for data discretization and label maps

A differentiable physics engine for robotics

A foolproof way of doing ML with GUI elements.

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Extreme Learning Machine in julia

Functions and data dependencies for loading various word embeddings (Word2Vec, FastText, GLoVE)

Empirical Risk Minimization in Julia.

Julia bindings for the Enzyme automatic differentiator

Boosted trees in Julia

Explainable AI in Julia.

Repository of best practices for deep learning in Julia, inspired by fastai

Repository housing feature selection algorithms for use with the machine learning toolbox MLJ.

Gradient based Machine Learning for Julia

I heard you like compile times