11. Suppose you want to optimize the sum-of-squares error for the sigmoid of a linear function.

(a) Modify the algorithm of Figure 7.8 so that the update is proportional to the gradient of the sum-of-suares error. Note that this question assumes you know differential calculus, in particular, the chain rule for differentiation.

(b) Does this work better than the algorithm that minimizes log loss when evaluated according to the sum-of-squares error?