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
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
Curated decibans of Julia programming language.
Created by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, Alan Edelman
Released February 14, 2012