Thanks for the reply and the resource! You're right about the relatively recent influx of people who enter the ML field via deep learning first. Seems like most of the intro material focuses on logistic sigmoid based methods.

That said, do you think there is a fundamental reason why other log likelihood based methods such as probit and poisson as you mentioned haven't caught on in the deep learning field? Is it just that probit doesn't give an edge in classification, and such a large portion of use cases don't require anything besides a classification based loss?