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CSC321 Lecture 10: Automatic Differentiation

CSC321 Lecture 10: Automatic DifferentiationRoger GrosseRoger GrosseCSC321 Lecture 10: Automatic Differentiation1 / 23 OverviewImplementing backprop by hand is like programming in ll probably never do it, but it s important for having a mentalmodel of how everything 6 covered the math of backprop, which you are using to codeit up for a particular network for Assignment 1 This Lecture : how to build an Automatic Differentiation (autodiff)library, so that you never have to write derivatives by handWe ll cover a simplified version of Autograd, a lightweight autodiff s autodiff feature is based on very similar GrosseCSC321 Lecture 10: Automatic Differentiation2 / 23 Confusing TerminologyAutomatic Differentiation (autodiff) refers to a general way of takinga progra

Mathematica’s derivatives for one layer of soft ReLU (univariate case): Derivatives for two layers of soft ReLU: There might not be a convenient formula for the derivatives. ... 4 = exp(t 3) t 5 = 1 + t 4 y = 1=t 5 t 6 = y t t 7 = t 2 6 L= t 7=2 Roger Grosse CSC321 Lecture 10: …

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