Part of the problem in writing a deep learning book, is that very little that warrants being in a book, is actually known. The mainstream deep learning academic community welcomes theoretical work but articles on new techniques which beat SOTA are given much more attention than articles on setting up a theoretical structure of the models. Consequently, that line of research is also under-cited and under-developed. In some ways, 'deep learning' is in a different Kuhnian paradigm altogether. So, people who are used to learning about all the intricacies of classical ML models fail to appreciate deep learning, because the metrics with which you would judge a deep learning model are different from the ones you would use to judge a classical ML model. The same applies for the books in different Kuhnian paradigm.