Causal, Higher-Order, Probabilistic Programming

Reverse-mode automatic differentiation in Julia

Implementation of domain-specific language (DSL) for dynamic probabilistic programming

Automatic differentiation of Distributions using Tracker, Zygote, ForwardDiff and ReverseDiff

Differentiable collision detection for polytopes, capsules, cylinders, cones, spheres, and polygons.

Differentiable RayTracing in Julia

A Julia framework for invertible neural networks

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

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A fast image augmentation library in Julia for machine learning.

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

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

System Identification toolbox, compatible with ControlSystems.jl

Proximal algorithms for nonsmooth optimization in Julia

Complex neural network examples for Flux.jl

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.

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

Perceptually Uniform Colour Maps for Julia

A package for Counterfactual Explanations and Algorithmic Recourse in Julia.

A Julia implementation of quaternions

Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous and/or discrete variables.

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.

Fast inference for Gaussian processes in problems involving time. Partly built on results from https://proceedings.mlr.press/v161/tebbutt21a.html

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Word cloud generator in julia

Starter kit for legendary models

Explainable AI in Julia.

Fast contact-implicit model predictive control for robotic systems that make and break contact with their environments.

MagNav: airborne Magnetic anomaly Navigation

MADS: Model Analysis & Decision Support

3D computer vision library in Julia

Core functions for the Julia (2nd) edition of the text Fundamentals of Numerical Computation, by Driscoll and Braun.

Beta Machine Learning Toolkit

Algorithms that combine tensor network methods with automatic differentiation

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

Dive deeper into your data with interactive graphics

Build flexible hierarchical multi-instance learning models.

Projective geometry for computer vision in Julia

OrbitalTrajectories.jl is a modern orbital trajectory design, optimisation, and analysis library for Julia, providing methods and tools for designing spacecraft orbits and transfers via high-performance simulations of astrodynamical models.