You clearly also haven't read many of the papers it cites.
I would say one weakness of the book is that parts of it are too much like a survey of the papers in a subfield. Another is that it is very heavy on theory and light on practice (e.g., no exercises.)
Pray tell me, oh self-conceited one, what I missed that is both in actual use and in that book ? For things outside this set, you'd not read this book anyway; nor would such things be called "deep learning" (other than may be RBMs).
