Geometry of Deep Learning : A Signal Processing Perspective /

The focus of this book is on providing students with insights into geometry that can help them understand deep learning from a unified perspective. Rather than describing deep learning as an implementation technique, as is usually the case in many existing deep learning books, here, deep learning is...

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Bibliographic Details
Main Author:	Ye, Jong Chul (Author)
Corporate Author:	SpringerLink (Online service)
Format:	eBook
Language:	English
Published:	Singapore : Springer Singapore : Imprint: Springer, 2022.
Edition:	1st ed. 2022.
Series:	Mathematics in Industry, 37
Subjects:	Functional analysis. Geometry, Differential. Artificial intelligence. Neural networks (Computer science) . Biomathematics. Functional Analysis. Differential Geometry. Artificial Intelligence. Mathematical Models of Cognitive Processes and Neural Networks. Mathematical and Computational Biology. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Part I Basic Tools for Machine Learning: 1. Mathematical Preliminaries
2. Linear and Kernel Classifiers
3. Linear, Logistic, and Kernel Regression
4. Reproducing Kernel Hilbert Space, Representer Theorem
Part II Building Blocks of Deep Learning: 5. Biological Neural Networks
6. Artificial Neural Networks and Backpropagation
7. Convolutional Neural Networks
8. Graph Neural Networks
9. Normalization and Attention
Part III Advanced Topics in Deep Learning
10. Geometry of Deep Neural Networks
11. Deep Learning Optimization
12. Generalization Capability of Deep Learning
13. Generative Models and Unsupervised Learning
Summary and Outlook
Bibliography
Index.