The problem is that there’s no clear way to change the weights of the neurons in the “hidden” layers to reduce the errors made by the ones in the output layer. Every hidden neuron influences the output via multiple paths, and every error has a thousand fathers. Who do you blame? Or, conversely, who gets the credit for correct outputs? This credit-assignment problem shows up whenever we try to learn a complex model and is one of the central problems in machine learning.
