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
Categories
Machine LearningFollow Zygote
Other Useful Business Software
Paessler - Monitor Your Whole Network in Minutes
Waiting weeks for a monitoring rollout isn't an option when infrastructure doesn't stop running. PRTG's auto-discovery scans your network and suggests from over 200 pre-configured sensor types, so you're watching servers, applications and devices within minutes, not after a multi-week deployment. Enterprise-strength monitoring, without the enterprise complexity. Start your free trial today.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of Zygote!