Section 19.6.5 (page 684) noted that the output of the logistic function could be interpreted as a probability p assigned by the model to the proposition that f(x) = 1; the probability that f(x) = 0 is therefore 1 − p. Write down the probability p as a function of x and calculate the derivative of log p with respect to each weight w_i. Repeat the process for log(1−p). These calculations give a learning rule for minimizing the negative-log-likelihood loss function for a probabilistic hypothesis. Comment on any resemblance to other learning rules in the chapter.