Maybe I'm biased specifically because of the book I mentioned. For me it's providing a theoretical basis for many things that earlier I learned in a hand-wavy way (e.g. way back I took Hinton's NN course and Ng's ML course, and learned about gradient descent, momentum, regularization, training/validation loss etc, and now with this book for the first time I feel like I get the bigger picture in the context of optimization/stats).

The previous version of this book was from 2012 though and I'm not 100% sure how much of the material in the current edition is new (there is definitely a _lot_ more deep learning stuff in it).

So yeah it could be that my impression is wrong, or that I made the scope at which it applies sound bigger than it is.