MXNet Julia Package - flexible and efficient deep learning in Julia

Overdub Your Julia Code

Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.

Heterogeneous programming in Julia

An implementation of the Microsoft Language Server Protocol for the Julia language.

Web-based GUIs for Julia

A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.

Makes Julia reason with equations. General purpose metaprogramming, symbolic computation and algebraic equational reasoning library for the Julia programming language: E-Graphs & equality saturation, term rewriting and more.

A package that makes it trivial to create and evaluate machine learning pipeline architectures.

Plotting for Julia based on GR, a framework for visualisation applications

Time series toolkit for Julia

A Julia package for data clustering

Julia implementation of Decision Tree (CART) and Random Forest algorithms

Special mathematical functions in Julia

Reverse Mode Automatic Differentiation for Julia

Geometric Deep Learning for Flux

Visualization of Julia profiling data

Metal programming in Julia

Taylor polynomial expansions in one and several independent variables.

Root finding functions for Julia

A comprehensive open source computer algebra system for computations in algebra, geometry, and number theory.

Probabilistic Programming with Gaussian processes in Julia

Finite element toolbox for Julia

A Julia Basket of Hand-Picked Krylov Methods

Auto QUality Assurance for Julia packages

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

A fast trajectory optimization library written in Julia

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

Surrogate modeling and optimization for scientific machine learning (SciML)

Computer vision models for Flux

Julia solvers for systems of nonlinear equations and mixed complementarity problems

Evolutionary & genetic algorithms for Julia

The reactive UI library for interactive data applications with pure Julia.

Modeling and simulation of multidomain engineering systems

Efficient implementation of struct arrays in Julia

Call R from Julia

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

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Reusable array functionality for Julia's various GPU backends.

🏔️Manopt. jl – Optimization on Manifolds in Julia