03 · How Models Learn
Backprop
Backpropagation
The standard algorithm for calculating how each trainable parameter contributed to an error, working backward through a differentiable network. An optimizer then uses those gradients to update the parameters.
Concrete example
After a wrong guess, backprop traces the mistake back through every layer to decide what to tweak.