Theoretical books about deep learning Can you please cite a book that treats deep learning from the mathematical perspective as I tried to search, but most of the books are either technical or too superficial in terms of the mathematics behind?
What are the best books to study Neural Networks from a purely mathematical perspective? I am looking for a book that goes through the mathematical aspects of neural networks, from simple forward passage of multilayer perceptron in matrix form or differentiation of activation functions, to back propagation in CNN or RNN (to mention some of the topics).  Do you know any book that goes in depth into this theory? I've had a look at a couple (such as Pattern Recognition and Machine Learning by Bishop or Deep Learning by Goodfellow, Bengio and Courville) but still have not found a rigorous one (with exercises would be a plus). Do you have any suggestions?
What is the lowest level of mathematics required in order to understand how encryption algorithms work? What mathematical fields of knowledge would be required in order to get a good understanding of encryption algorithms?   Is it basic algebra, or is there a "higher education" mathematical field dedicated to encryption? I know there is the cryptography field, but what is the subset of knowledge required for cryptographers?