9. Gradient Descent [8 points] For a homework assignment, you have implemented a logistic regression model y() = o(Wx() + b) You decide to train your model on a multi-class problem using gradient descent. How- ever, before you can turn your assignment in, your friends stop by to give you some suggestions. (a) [2 points] Pat sees your regressor implementation and says there's a much simpler way! Just leave out sigmoids, and let f(x) = Wx+b. The derivatives are a lot less hassle and it runs faster. What's wrong with Pat's approach? (b) [2 points] Chris comes in and says that your regressor is too complicated, but for a different reason. The sigmoids are confusing and basically the same answer would be computed if we used step functions instead. What's wrong with Chris's approach?