Julia bindings for the Enzyme automatic differentiator

Primitives for construction of objects

An experimental code analyzer for Julia. No need for additional type annotations.

Arrays with arbitrarily nested named components.

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.

Forward Mode Automatic Differentiation for Julia

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

Julia macro for re-exporting one module from another

Julia wrapper for the LLVM C API

Formatted output of timed sections in Julia

21st century AD

Heterogeneous programming in Julia

Repository for automatic differentiation backend types

Data Transfer Functionalities across Backends for Machine Learning Applications

Interpreter for Julia code

Reusable compiler infrastructure for Julia GPU backends.

Tools for manipulating Julia's lowered code

Reduce time-to-first-execution of Julia code

Compatibility across Julia versions

Extensions for Julia's docsystem.

Reusable array functionality for Julia's various GPU backends.

Julia package for lazily representing matrices filled with a single entry

Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations

Neural Network primitives with multiple backends

Statically sized arrays for Julia

An interface for tables in Julia

Interface designs for enforcing static computations in array functions with Julia

The dispatch doctor prescribes type stability

Reverse Mode Automatic Differentiation for Julia

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

Flux's ex AD

A Julia framework for implementing FFTs

Julia implementation of associative containers that preserve insertion order

Interface for function inversion in Julia

SparseArrays.jl is a Julia stdlib

Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support

Efficient implementation of struct arrays in Julia

Naïve combined subexpression elimination in Julia

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Interface for transformation functions in Julia