@nusco

Programming Machine Learning
From Coding to Deep Learning
German edition

Chapter 11
Training the network
Page 191

def back(X, Y, y_hat, w2, h): w2_gradient = np.matmul(prepend_bias(h).T, y_hat - Y) / X.shape[0] a_gradient = np.matmul(y_hat - Y, w2[1:].T) * sigmoid_gradient(h) w1_gradient = np.matmul(prepend_bias(X).T, a_gradient) / X.shape[0] return (w1_gradient, w2_gradient) 

Hi Paolo,
can you explain me how to come to the decision to multiply this expression:
np.matmul(y_hat - Y, w2[1:].T) * sigmoid_gradient(h)
element by element?

Sincerely,
Kai