BackProp Algorithm: Pseudocode and Processing Steps

This lecture covers the BackProp algorithm, starting with the initialization of weights and the forward propagation of signals, followed by the computation of errors in the output, backward propagation of errors, updating weights, and the comparison with direct numerical evaluation of gradients. The complexity of BackProp and direct numerical differentiation is discussed, along with a quiz on BackProp and conclusions on multilayer networks.