Gradient Computation And Backpropagation

via “gradient-computation-and-backpropagation”

A guide to building your own working LLM, by Sebastian Raschka.

Unique: Walks through gradient computation step-by-step for each component, showing how chain rule applies through attention and FFN layers, and explains numerical stability tricks (gradient clipping, normalization)

vs others: More educational than relying on framework autograd, enabling practitioners to understand and debug gradient flow issues in custom architectures