Math and Architectures of Deep Learning

by Krishnendu Chaudhury

The mathematical paradigms that underlie deep learning typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function.

Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch.

Written by deep learning expert Krishnendu Chaudhury, you’ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications.

Genres: Artificial Intelligence, Computer Science

450 pages, Paperback

First published March 1, 2020

Reviews

[Guru · Rating 3 out of 5]

A good, summarized reference of all the maths behind Machine Learning techniques. It is not the most thorough but is a great refresher for vectors calculus, probability, Bayesian inference, etc. - and how all of these come together in ML in general and Deep Learning in particular.
I found the "math" part more lucid than the "architecture" part - Ian Goodfellow's book explains the Deep Learning architecture much better.