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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
Categories
Machine LearningFollow Zygote
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