Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

A flexible neural net training library inspired by fast.ai

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals

Combinatorial optimization layers for machine learning pipelines

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

Koma is a Pulseq-compatible framework to efficiently simulate Magnetic Resonance Imaging (MRI) acquisitions. The main focus of this package is to simulate general scenarios that could arise in pulse sequence development.

Utilities and abstractions for Machine Learning tasks

MagNav: airborne Magnetic anomaly Navigation

3D computer vision library in Julia

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

Robust implementation for random-walk Metropolis-Hastings algorithms

Build flexible hierarchical multi-instance learning models.

A Julia rewrite of Dynare: solving, simulating and estimating DSGE models.

Julia Package for MRI Reconstruction

Stochastic Optimization, Learning, Uncertainty and Sampling

High level functions for analyzing the output of simulations

Fast, scalable and flexible Outlier Detection with Julia

Abstract types and interfaces for Markov chain Monte Carlo methods

A Julia package to solve, simulate, and analyze nonlinear DSGE models.

WIP successor to Soss.jl

Preheat your MCMC

Implementation of the Deep Q-learning algorithm to solve MDPs

A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality

Bayesian Generalized Linear models using `@formula` syntax.

Global glacier model using Universal Differential Equations for climate-glacier interactions

Turing + Makie = Turkie

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

Visualization for Flux.Chain neural networks

Atomic graph models for molecules and crystals in Julia

Use Optim to train Flux models and visualize loss landscapes

Data-parallelism on CUDA using Transducers.jl and for loops (FLoops.jl)

Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms

Distributed Data Parallel Training of Deep Neural Networks

FMIFlux.jl is a free-to-use software library for the Julia programming language, which offers the ability to place FMUs (fmi-standard.org) everywhere inside of your ML topologies and still keep the resulting model trainable with a standard (or custom) FluxML training process.

A foolproof way of doing ML with GUI elements.

Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia

Finds relationships between the parameters of a mathematical model

A Julia package for robust neural networks.

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