Chapter 2: Backward - Automatic Gradient Computation

What You'll Build The Backward() method on Value . Starting from a final output (like the loss), it walks the computation graph in reverse and fills in the .Grad field on every Value , answering "if I nudged this number, how much would the final output change?" Depends On Chapter 1 (the Value class with forward operations). Why This Matters Training a neural network comes down to: "nudge each parameter a tiny bit in the direction that lowers the loss." But with thousands of parameters and a comp