I am surprised by all the criticism. Sure, this does not make the point perfectly (with various details one could nitpick), but the basic premise seems complete

I am surprised by all the criticism. Sure, this does not make the point perfectly (with various details one could nitpick), but the basic premise seems completely agreeable and even boring - there are ML and statistics techniques that are simpler, more interpretable, and faster than deep learning ones that can often be sufficiently robust for various problems/goals (SVMs and random trees/forests in particular are lovely). At least, that's how it seemed to me. The wording might be improved to alter "when your dataset isn’t that big" to "when you target function isn’t that complex/when your data isn't that complex", sure, but the point stands.