Cost Function, Non Convex Optimization, Logistic Regression, Log Loss Function.
absolutely got to be Y equals 1. But if Y turned out to be equal to 0 then it makes sense to make the hypothesis, or make the learning algorithm pay a very large cost. And conversely, if H of X is equal to 0 and Y equals zero, then the hypothesis nailed it. The predicted Y is equal to zero and it