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

Powerful convenience for Julia visualizations and data analysis

21st century AD

Forward Mode Automatic Differentiation for Julia

Statically sized arrays for Julia

Macro(s) for vectorizing loops.

Progress meter for long-running computations

Julia implementation of Data structures

HTTP for Julia

Physical quantities with arbitrary units

Basic statistics for Julia

Convert julia objects to LaTeX equations, arrays or other environments.

HDF5-compatible file format in pure Julia

Fast, continuous interpolation of discrete datasets in Julia

An optimized graphs package for the Julia programming language

Forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs

A Julia package for evaluating distances (metrics) between vectors.

Types with default field values, keyword constructors and (un-)pack macros

High performance nearest neighbor data structures (KDTree and BallTree) and algorithms for Julia.

Julia functional programming infrastructures and metaprogramming facilities

Heterogeneous programming in Julia

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

A Julia package for data clustering

Special mathematical functions in Julia

Efficient implementation of struct arrays in Julia

Reusable array functionality for Julia's various GPU backends.

JSON parsing and printing

MacroTools provides a library of tools for working with Julia code and expressions.

Polynomial manipulations in Julia

An interface for tables in Julia

Julia bindings to the FFTW library for fast Fourier transforms

AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.

Main Package for IO, loading all different kind of files

Convenient input and display of LaTeX equation strings for the Julia language

Color manipulation utilities for Julia

Reduce time-to-first-execution of Julia code

Neural Network primitives with multiple backends

Performant arrays where each dimension can have a named axis with values

Fortran-like arrays with arbitrary, zero or negative starting indices.

Lazy code loading for Julia