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.
Instructions: Describe methods for securing Python code. Pick at least ONE of the methods for securing node and deep dive into what it means and
Instructions: Describe methods for securing Python code. Pick at least ONE of the methods for securing node and deep dive into what it means and how it is used to secure code.