[spi]

Sharing my experience here. My background is in math (Ph.D. and a couple of postdoc years) before switching to practitioner in deep learning. This year I taught a class at university (as invited prof) in deep learning for students doing a masters in math and statistics (but with some programming knowledge, too).

I tried to present concepts in an as reasonably accurate mathematical way as possible, and in the end I cut through a lot of math in part to avoid the heavy notation which seems to be present in this book (and in part to make sure students could spend what they learnt in the industry). My actual classes had way more code than formulas.

If you want to write everything very accurately, things get messy, quickly. Finding a good notation for new concepts in math is very hard, something that gets sometimes done by bright minds only, even though afterwards everybody recognizes it was “clear” (think about Einstein notation, Feynman diagrams, etc., or even just matrix notation, which Gauss was unaware of). If you just take domain A and write in notations from domain B, it’s hard to get something useful (translating quantum mechanics to math with C* algebras and co. was a big endeavour, still an open research field to some extent).

So I’ll disagree with some of the comments below and claim that the effort of writing down this book was huge but probably scarcely useful. Who can read comfortably these equations probably won’t need them (if you know what an affine transformation is, you hardly need to see all its ijkl indices written down explicitly for a 4-dimensional tensor), and the others will just be scared off. There might be a middle ground where it helps some, but at least I haven’t encountered such people…