Placeholder text

Go to cart

The Principles of Deep Learning Theory

By Roberts, Daniel A.

The Principles of Deep Learning Theory

By Roberts, Daniel A.

0 - Default Title
Description
This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first-principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self-contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus, and informal probability theory, and it can easily fill a semester-long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning.
Product details
Number of Pages:
472
Release Date:
2022-06-30
Publication Date:
2022-04-15
Publisher:
Cambridge University Press
Languages:
Original: English
ISBN10:
1316519333
ISBN13:
9781316519332
GPSR Manufacturer Reference:
[email protected]
Weight:
1075 g
Height:
183 cm
Width:
260 cm
Thickness:
30 cm
Buy on Amazon
Currently sold out