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

A general-purpose probabilistic programming system with programmable inference

21st century AD

A Julia package for probability distributions and associated functions.

Forward Mode Automatic Differentiation for Julia

Statically sized arrays for Julia

Julia implementation of Data structures

Basic statistics for Julia

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

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

Julia functional programming infrastructures and metaprogramming facilities

Heterogeneous programming in Julia

Special mathematical functions in Julia

Reverse Mode Automatic Differentiation for 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.

An interface for tables in Julia

Adaptive 1d numerical Gauss–Kronrod integration in Julia

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.

Mathematical functions related to statistics.

Reduce time-to-first-execution of Julia code

Neural Network primitives with multiple backends

Lazy code loading for Julia

Julia package for lazily representing matrices filled with a single entry

Update immutable data

Update deeply nested immutable structs.

Julia macro for re-exporting one module from another

Simple Traits for Julia

Compatibility across Julia versions

Julia wrapper for the LLVM C API

Project Preferences Package

A Julia framework for implementing FFTs

Functional and persistent data structures for Julia

Parameterise all the things

Extensions for Julia's docsystem.

Fast parsing machinery for basic types in Julia

Mike's Little Intermediate Representation

Utilities and abstractions for Machine Learning tasks