Backpropagation Algorithm Step By Step Walkthrough

via “backpropagation algorithm derivation and implementation”


Unique: Derives backpropagation from first principles using the chain rule, then shows the computational implementation that makes it efficient (storing activations, computing gradients in reverse topological order), making the connection between mathematical theory and practical algorithm explicit

vs others: More rigorous mathematical treatment than most tutorials, more accessible than academic papers, and includes working code alongside derivations unlike pure theory courses

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

via “backpropagation-algorithm-step-by-step-walkthrough”


Unique: Uses concrete numerical examples with small networks to show exactly how each weight is updated, making backpropagation transparent by tracing gradients step-by-step rather than presenting it as a mathematical abstraction. Videos show the chain rule applied in context, not just as an equation.

vs others: More concrete than textbook explanations, and more rigorous than oversimplified blog posts that skip important details

via “backpropagation-algorithm-instruction”