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
Analytical Web Apps for Python, R, Julia, and Jupyter. No JavaScript Required.
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.
Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
Visualizations and plotting in Julia
Powerful convenience for Julia visualizations and data analysis
A Julia machine learning framework
In-memory tabular data in Julia
Koç University deep learning framework.
21st century AD
Package to call Python functions from the Julia language
Curated decibans of Julia programming language.
Concise and beautiful algorithms written in Julia
Created by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, Alan Edelman
Released February 14, 2012