Julia is a high-level dynamic programming language designed to address the needs of high-performance numerical analysis and computational science. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.
The Julia Programming Language
🎈 Simple reactive notebooks for Julia
App to easily query, script, and visualize data from every database, file, and API.
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
High-Performance Symbolic Regression in Python and Julia
Interactive data visualizations and plotting in Julia
🧞The highly productive Julia web framework
Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
Powerful convenience for Julia visualizations and data analysis
A Julia machine learning framework
In-memory tabular data in Julia
21st century AD
Package to call Python functions from the Julia language
Curated decibans of Julia programming language.
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Created by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, Alan Edelman
Released February 14, 2012