Starter kit for legendary models

Bayesian Non-negative Matrix Factorization

Differentiate python calls from Julia

OLLAM: Online Learning of Linear Adaptable Models

Use Optim to train Flux models and visualize loss landscapes

LIBLINEAR bindings for Julia

A foolproof way of doing ML with GUI elements.

Deep Learning for Julia

Utility package for generating, loading, splitting, and processing Machine Learning datasets

Machine learning kernels in Julia.

Machine learning kernels in Julia.

A operator overloading, tape-based, reverse-mode AD

Transformations to contrained variables from ℝⁿ.

Utility package for subsetting, resampling, iteration, and partitioning of various types of data sets in Machine Learning

Automatic detection of sparsity in pure Julia functions for sparsity-enabled scientific machine learning (SciML)

Turing + Makie = Turkie

Reverse-mode automatic differentiation in Julia

Automatic probabilistic programming for scientific machine learning and dynamical models

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

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Evolve Flux networks from scratch!

Heterogeneous ensemble learning for Julia.

A set of functions to support the development of machine learning algorithms

Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.

Workshop materials for training in scientific computing and scientific machine learning

Core functionality for the MLJ machine learning framework

MXNet Julia Package - flexible and efficient deep learning in Julia

BayesianNonparametrics in julia

A Julia library for handling MIDI files

Sleek implementations of the ZigZag, Boomerang and other assorted piecewise deterministic Markov processes for Markov Chain Monte Carlo including Sticky PDMPs for variable selection

Generic UNet implementation written in pure Julia, based on Flux.jl

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

A 100%-Julia implementation of Gradient-Boosting Regression Tree algorithms

Machine Learning in Julia

LIBSVM bindings for Julia

Lightweight package to interface with MLJ

DEPRECATED: Regularised Empirical Risk Minimisation Framework (SVMs, LogReg, Linear Regression) in Julia

Cartesian Genetic Programming for Julia

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

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