Machine Learning, Deep Learning, Artificial Intelligence, Regularization, Deep Learning Book.
(w*)= 0), we get the following expression (as the first order gradient term is 0): Finally, ∇ Ĵ(w) = H(w — w*) since the first term is just a constant and the derivative of X’ H X (’ represents the transpose) is 2 H X. The overall gradient of the objective function (gradient of Ĵ + gradient of