A guided example. Follow one cat image through a small deep network: forward pass, error, backpropagation, weight update, and watch the prediction go from wrong to correct as the loss falls.
Teaching simplification: this view follows one example so the idea stays visible. Real training uses mini-batches of many examples, averages their gradients, then updates the shared weights, repeated over many epochs.