10. Consider Equation 7.1, which gives the error of a linear prediction.

(a) Give a formula for the weights that minimize the error for the case where n = 2 (i.e., when there are only two input feature). [Hint: For each weight, differentiate with respect to that weight and set to zero.]

(b) Why is it hard to minimize the error analytically when using a sigmoid function as an activation function, for n = 2? (Why doesn’t the same method as in part

(a) work?)