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

Evolve Flux networks from scratch!

Use Optim to train Flux models and visualize loss landscapes

Wrapping deep learning models from the package Flux.jl for use in the MLJ.jl toolbox

Flux's ex AD

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

A package for time series data processing, classification, clustering, and prediction.

Easy peasy logging to TensorBoard with Julia

Boosted trees 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

Core functionality for the MLJ machine learning framework

Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.

Bayesian optimization for Julia

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

A Julia machine learning framework

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

Reverse-mode automatic differentiation in Julia

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

Transformations to contrained variables from ℝⁿ.

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

Run keras models with a Flux backend

Build flexible hierarchical multi-instance learning models.

Computer vision models for Flux

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

I heard you like compile times

A Julia package for training and evaluating multimodal deep Boltzmann machines

Machine Learning in Julia

Empirical Risk Minimization in Julia.

Evaluating Robustness of Neural Networks with Mixed Integer Programming

Baseline collective classification library

Uniform interface for machine learning in Julia

Cartesian Genetic Programming for Julia

Julia tools for feeding tabular data into machine learning.

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

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

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

A generic and modular framework for building custom iterative algorithms in Julia

Simple particle filter implementation in Julia - works with POMDPs.jl models or others.

Utility package for working with classification targets and label-encodings

BayesianNonparametrics in julia