input features : X ERP. target labels (one-hot encoded): y {0,1}K. multinomial linear classifier : f=Wx+b, WERKxD and f,bERK e.g., for the k-th classification : fk = w x + bk, corresponding to yk, where w is the k-th row of W, k {1...K} T = (a) Please express the softmax loss of logistic regression, L(x, W, b, y), using the above notation. (b) Please calculate its gradient derivative al awk input features : X ERP. target labels (one-hot encoded): y {0,1}K. multinomial linear classifier : f=Wx+b, WERKxD and f,bERK e.g., for the k-th classification : fk = w x + bk, corresponding to yk, where w is the k-th row of W, k {1...K} T = (a) Please express the softmax loss of logistic regression, L(x, W, b, y), using the above notation. (b) Please calculate its gradient derivative al awk