
Deep Learning Architectures
A Mathematical Approach
Ovidiu Calin(Author)
Springer (Publisher)
Published on 14. February 2021
Book
Paperback/Softback
XXX, 760 pages
978-3-030-36723-7 (ISBN)
Description
This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter.
This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.
Reviews / Votes
"This book is useful to students who have already had an introductory course in machine learning and are further interested to deepen their understanding of the machine learning material from the mathematical point of view." (T. C. Mohan, zbMATH 1441.68001, 2020)More details
Product info
Book
Series
Edition
1st ed. 2020
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
172
35 farbige Abbildungen, 172 s/w Abbildungen
XXX, 760 p. 207 illus., 35 illus. in color.
Dimensions
Height: 254 mm
Width: 178 mm
Thickness: 43 mm
Weight
1460 gr
ISBN-13
978-3-030-36723-7 (9783030367237)
DOI
10.1007/978-3-030-36721-3
Schweitzer Classification
Other editions
Additional editions

Book
02/2020
Springer
€106.99
Shipment within 7-9 days
Person
Ovidiu Calin, a graduate from University of Toronto, is a professor at Eastern Michigan University and a former visiting professor at Princeton University and University of Notre Dame. He has delivered numerous lectures at several universities in Japan, Hong Kong, Taiwan, and Kuwait over the last 15 years. His publications include over 60 articles and 8 books in the fields of machine learning, computational finance, stochastic processes, variational calculus and geometric analysis.
Content
Introductory Problems.- Activation Functions.- Cost Functions.- Finding Minima Algorithms.- Abstract Neurons.- Neural Networks.- Approximation Theorems.- Learning with One-dimensional Inputs.- Universal Approximators.- Exact Learning.- Information Representation.- Information Capacity Assessment.- Output Manifolds.- Neuromanifolds.- Pooling.- Convolutional Networks.- Recurrent Neural Networks.- Classification.- Generative Models.- Stochastic Networks.- Hints and Solutions.