[mjw]

If anything, to me a lot of deep learning literature seems to lack the statistical insight and theory that's available to other subfields in machine learning (whether the Bayesian/graphical models camp, the statistical learning theory camp...)

If this book is trying to do more to bring statistical or probabilistic insights to bear on deep learning than I think that's a very good thing. It might make it less accessible to those coming from a pure computer science background, but potentially more so to those who like to think about machine learning from a probabilistic modelling perspective.

If they're using stats jargon in a gratuitous way that doesn't actually cast any light on the material then that's another thing, but from a quick skim I didn't see anything particularly bad on this front. Do you have any examples of the kind of jargon you're talking about?

To others reading, I just wanted to emphasise that statistics is really important in machine learning! Deep learning lets you get away with less of it than you might need elsewhere, but that doesn't mean one can treat it as an unnecessary inconvenience. It's a language you need to learn, especially if you want to try and get to the bottom of how and why aspects of deep learning work the way they do. As opposed to just an empirical "using GPU clusters to throw lots of clever shit at the wall and see what sticks" engineering field. Bengio seems very interested in these kinds of questions and I'm glad he's leading research in that direction, even if clear answers and intuition aren't always easy to come by at this point.