7. 9 points] Suppose we have the training data shown in Table from which we want to learn a linear regression model, parameterized by a weight vector w and a bias parameter b (a) 1 point Write down the LMS (least mean square) cost function J(w,b) (b) [3 points] Calculate the gradient and i. when w [0,0,0]T and b0; 1 1 21 3 1 -10 Table 1: Linear regression training dat:a 11, when w 111, when w 1-1 1,-1]T and b =-1; 11/2,-1/2, 1/2] T and b 1. (c) 2 points] What are the optimal w and b that minimize the cost function? (d) [3 points Now, we want to use stochastic gradient descent to minimize J(w, b) We initialize w 0 and b 0. We set the learning rate r 0.1 and sequentially go through the 5 training examples. Please list the stochastic gradient in each step and the updated w andb 7. 9 points] Suppose we have the training data shown in Table from which we want to learn a linear regression model, parameterized by a weight vector w and a bias parameter b (a) 1 point Write down the LMS (least mean square) cost function J(w,b) (b) [3 points] Calculate the gradient and i. when w [0,0,0]T and b0; 1 1 21 3 1 -10 Table 1: Linear regression training dat:a 11, when w 111, when w 1-1 1,-1]T and b =-1; 11/2,-1/2, 1/2] T and b 1. (c) 2 points] What are the optimal w and b that minimize the cost function? (d) [3 points Now, we want to use stochastic gradient descent to minimize J(w, b) We initialize w 0 and b 0. We set the learning rate r 0.1 and sequentially go through the 5 training examples. Please list the stochastic gradient in each step and the updated w andb