This article introduces Micrograd.jl, a new automatic differentiation package for the Julia programming language. It aims to fill a gap in comprehensive tutorials for AD in Julia, requiring a solid understanding of both Julia and Calculus. The package is built upon Zygote.jl and ChainRules.jl, offering a different approach to AD compared to Python frameworks like PyTorch by leveraging Julia's functional programming and metaprogramming capabilities. AI
影响 Provides a new tool for Julia developers to build and train machine learning models, potentially improving efficiency and understanding of backpropagation.
排序理由 This is a technical article detailing the creation of a new automatic differentiation package for Julia, including its underlying principles and dependencies. [lever_c_demoted from research: ic=1 ai=1.0]
在 HN — machine learning stories 阅读 →
- Andrej Karpathy
- ChainRulesCore.jl
- ChainRules.jl
- Flux.jl
- IRTools.jl
- Julia
- Micrograd.jl
- PyTorch
- Zygote.jl
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