I am doing Andrew Ng's excellent new Deeplearning course. I have an issue with implementing back propagation in a one hidden layer network that looks like this:I am trying to derive the derivative of Z1 using the formula:My understanding is that g prime Z1 in this formula is the derivative of g(z) with respect to Z which I believe is    sigmoid(Z) * (1-sigmoid(Z))where the function sigmoid generates the sigmoid function with respect to Z.My final equation looks like this:  dZ1 = np.dot(W2.T, dZ2) * (sigmoid(Z1)*(1-sigmoid(Z1)))Where Z1 is the output of the first (and only) hidden layer. Where am I going wrong?
Neural networks: generating g prime(z) in back propagation
neural network;backpropagation