Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)

If you are interested in the mathematical aspects of deep learning, especially those more advanced ones connecting it with various mathematical representations, then this book is highly recommended. Almost 700 pages of rich math but very well explained. Rigorous proofs are illuminating and provide the theoretical background necessary to elevate one from a good ML practitioner to a great one.

I think this is good book for deep learning method. Highly recommended it.

Good content to read. Useful for those who like theory behind boilerplate code. Shipping is really bad, my new book's corner is seriously damaged

Poorly written, haphazardly organized, book.

The book seemed to be nice. It has a good approach on explanation on convergence of neural networks and some geometric implication of the models.But the book is in overall sense, horrible.The author often makes wrong notations.Ex. Mistaking x with x*, suddenly introducing a new aphabetical symbo D, when it is suposed to be B. Wrong directions of mathematical operator such as set inclusion, set substraciton, etc.The author mentions some un-explaned terms, which seems to be only used by him in his own imagination or his lectures.I tried to figure out the meaning of "digenerate direction" for an hour.These mistakes make the reader hard to figure out what the hack is going on.Sometimes I had to figure out which one of the statements are mistake, and it gives me headaches.There was a wrong proof.Lemma 4.2.2.It was a non-rigorous proof which made assumed some approximation,but even that non-rigorous proof had wrong logic, I figured out my own proof to correct the problem.Some statements omit some important assumptions(such as the function does not have multiple local minimum or local maximum) which make the statements wrong.There are so many "so what?" lemmas and prepositions.I don't know what to do with some lemmas and prepositions.The author broght up some lemmas and prepositions, and those do not give any explanation or ideas related to the the topics of the chapters.I stopped reading book at the chapters about information theory.The author states that (I have made the statements simpler, so that it is easier to understand)1. a network that outputs only recoverable information is a network that has lost all information.2. a network that has lost all information is uselss.3. a network that has lost all informmation does not tell any information.statement 2 is never explained why.statement 3 is contradictory with 1,According to 1, a network that has lost all information may output recoverable information,but the author suddenly statets that the netowrk does not give any information.It seems the author was hallucinating while writhing this chapter.I spent some money to buy this book. I am disappointed and had some headaches figuring out the correction of the errors made by the author.To author,if you are selling a book, you should have done some proof readings before publishing it. People are spending money to buy the book.