Abstract types and methods for Gaussian Processes.

Adaptive Skip-gram implementation in Julia

Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms

A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.

Julia artificial neural networks

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

Burkhard-Keller trees implementation

Bayesian Non-negative Matrix Factorization

Restricted Boltzmann Machines in Julia

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

Visualization for Flux.Chain neural networks

A Julia package for data clustering

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

A minimal reinforcement learning environment interface with additional opt-in features.

Julia API for ConceptNetNumberbatch

Confidence weighted classifier

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

Experimental automated machine learning for Julia.

A variety of loaders for various NLP corpora.

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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Implementation of the Deep Q-learning algorithm to solve MDPs

Dependency parsing in Julia

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

Example of distributed learning in Julia

A differentiable physics engine for robotics