You’re not doing calculus on a graph- you’re using a graph algorithm to automate the derivative taking process. Essentially, you transform your function into a

You’re not doing calculus on a graph- you’re using a graph algorithm to automate the derivative taking process. Essentially, you transform your function into a “circuit” or just a graph with edge labels according to the relationship between parts of the expression. The circuit has the nice property that there is an algorithm you can run on it, with very simple rules, which gets you the derivative of the function used to create that circuit. So taking the derivative becomes: 1. Transform function F into circuit C. 2. Run compute_gradiant(c) to get the gradient of F. Lots of useful examples here: https://cs231n.github.io/optimization-2/ https://cs231n.github.io/optimization-2/