Easy peasy logging to TensorBoard with Julia

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

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

High-performance reactive message-passing based Bayesian inference engine

Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet

Julia FFI interface to Microsoft's LightGBM package

Beta Machine Learning Toolkit

Bayesian optimization for Julia

OpenAI API wrapper for Julia

A Julia package for non-negative matrix factorization

Pure Julia implementations of single-pass object detection neural networks.

LIBSVM bindings for Julia

Build flexible hierarchical multi-instance learning models.

A Julia package for interpretable machine learning with stochastic Shapley values

Unofficial Julia bindings for logging experiments to wandb.ai

Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)

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

Home of the MLJ model registry and tools for model queries and mode code loading

Machine learning kernels in Julia.

Machine learning kernels in Julia.

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

Cartesian Genetic Programming for Julia

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

Turing + Makie = Turkie

A Julia library for handling MIDI files

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

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

Transformations to contrained variables from ℝⁿ.

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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)

Use Optim to train Flux models and visualize loss landscapes

Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms

Differentiate python calls from Julia

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

A foolproof way of doing ML with GUI elements.

Flux's ex AD

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

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"Maybe we have our own magic."