i mean for many classic ML models you have this — literally big O notation for the number of samples required to learn, number of iterations to convergence, cos

i mean for many classic ML models you have this — literally big O notation for the number of samples required to learn, number of iterations to convergence, cost per iteration, etc. You even get the cases where the algorithm with the best big-O bound is totally impractical due to constant factors. The problem is just that all this goes out the window for deep learning, because nobody really knows how to prove much of anything about neural networks.