Zygote provides source-to-source automatic differentiation (AD) in Julia, and is the next-gen AD system for the Flux differentiable programming framework. For more details and benchmarks of Zygote's technique, see our paper. You may want to check out Flux for more interesting examples of Zygote usage; the documentation here focuses on internals and advanced AD usage.

Features

  • Zygote supports Julia 1.6 onwards, but we highly recommend using Julia 1.8 or later
  • Zygote supports the flexibility and dynamism of the Julia language, including control flow, recursion, closures, structs, dictionaries, and more
  • Zygote benefits from using the ChainRules.jl ruleset
  • Custom gradients can be defined by extending the ChainRulesCore.jl's rrule
  • To support large machine learning models with many parameters, Zygote can differentiate implicitly-used parameters
  • Examples available

Project Samples

Project Activity

See All Activity >

Categories

Machine Learning

Follow Zygote

Zygote Web Site

Other Useful Business Software
$300 Free Credits to Build on Google Cloud Icon
$300 Free Credits to Build on Google Cloud

New customers can spin up VMs, build with AI, and query data at no cost.

Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
Start Free
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of Zygote!

Additional Project Details

Programming Language

Julia

Related Categories

Julia Machine Learning Software

Registered

2023-11-02