Relax! Flux is the ML library that doesn't make you tensor

🎈 Simple reactive notebooks for Julia

Julia kernel for Jupyter

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components

Crafty statistical graphics for Julia.

A general-purpose probabilistic programming system with programmable inference

Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)

Koç University deep learning framework.

Powerful convenience for Julia visualizations and data analysis

Deep Learning framework for Julia

The highly productive Julia web framework

Bayesian inference with probabilistic programming.

A Julia machine learning framework

In-memory tabular data in Julia

Package to call Python functions from the Julia language

Intimate Affection Auditor

High level plotting on the GPU.

A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.

A Julia wrapper for TensorFlow

Compile your Julia Package

Unicode-based scientific plotting for working in the terminal

Automatically update function definitions in a running Julia session

Optimization functions for Julia

A Julia package for probability distributions and associated functions.

Parallel analytical database in pure Julia

Bootstrap components for Plotly Dash

An optimized graphs package for the Julia programming language

Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)

A 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

The Julia C++ Interface

Scientific reports/literate programming for Julia

Scientific reports/literate programming for Julia

A fast and modern CAS for a fast and modern language.

⚡ Single-pass algorithms for statistics ⚡

Extensible, Efficient Quantum Algorithm Design for Humans.

Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

Forward Mode Automatic Differentiation for Julia

Syntax highlighting and other enhancements for the Julia REPL

(yet another) static site generator. Simple, customisable, fast, maths with KaTeX, code evaluation, optional pre-rendering, in Julia.

(yet another) static site generator. Simple, customisable, fast, maths with KaTeX, code evaluation, optional pre-rendering, in Julia.