Forward Pass and Computational Graphs
A neural network is a function composition, layer feeding layer, and the graph of that composition is exactly what gets differentiated to train the network. Writing the forward pass explicitly as a graph of small, primitive operations is what turns "compute gradients" from a calculus exercise into a completely mechanical procedure — this page establishes that graph view before Backpropagation differentiates it.