Derive the gradient descent training rule assuming for the target function $o_d=w_0+w_l{x}_l+$dots$+w_n{x}_n.$ Define explicitly the squared cost$/$error function $E,$ assuming that a set of training examples $D$ is provided, where each training example $dD$ is associated with the target output $t_d.$