Part 1 of this series on automatic differentiation in Julia introduces explicit chain rules. Other parts cover automation with expressions, intermediate representation, extensions, and multi-layer perceptrons. Automatic differentiation is crucial in modern machine learning frameworks, handling forward and backward passes for model training. PyTorch and Flux.jl automate the backward pass, ensuring consistency with the forward pass. Zygote.jl, based on Julia’s functional programming, offers true automatic differentiation without extra effort. While powerful, Zygote.jl can be complex and buggy. The series aims to fill the gap in Julia’s automatic differentiation tutorials. Flux.jl, ChainRules.jl, and Zygote.jl form the core of Julia’s AD ecosystem. Controversially, Zygote pushes the boundaries of metaprogramming in Julia. The series focuses on backpropagation and recreating functions like rrule for different operations. Trigonometry and polynomial curve fitting are covered, showcasing the efficiency of calculating forward and backward passes together. The series provides insights for building AD packages in Julia.
https://liorsinai.github.io/machine-learning/2024/07/27/micrograd-1-chainrules.html