The problem is that epsilon deltas have very little practical use outside of theoretical proofs in pure mathematics. Even for cutting edge CS/statistics fields like high level machine learning, most of the calculus used are existing formalisms on multidimensional statistics and perhaps differential equations. Aside from Jensen's inequality and the mean value theorem, I have never seen any truly useful epsilon delta proofs being used in any of the ML papers with significant impact. It's perhaps mentioned once in passing when teaching gradient descent to grad students.