1. Consider a three layer neural network with one output unit, 10 hidden units at each hidden layer, and a five dimensional augmented feature vector as inputs. (a) Draw the network. (b) Write down the general expression on the derivative of the error function with respect to a weight using back error propagation (i.e., gradient decent). (c) Verify the general expression by calculating the derivative of the error function with respect to a weight that connects the 5th hidden unit at the first hidden layer with the 6 hidden unit at the second hidden layer. (d) Write a pseudo algorithm that computes the increment of a weight that connects the 4th input component with the 3rd hidden unit at the first hidden layer