Section 19.6.5 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 wi. 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.
in the files are the screenshots of my learning styles questionnaire The simple online learning style inventory you took this week, Index of Learning
in the files are the screenshots of my learning styles questionnaire The simple online learning style inventory you took this week, Index of Learning Styles QuestionnaireLinks to an external site., evaluated whether you are active or reflective, sensing or intuitive, visual or verbal, or sequential or global. For this discussion,