Deep Learning and Physics
Authors: Tanaka, Akinori, Tomiya, Akio, Hashimoto, Koji
Free Preview Is the first machine learning textbook written by physicists so that physicists and undergraduates can learn easily
 Presents applications to physics problems written so that readers can soon imagine how machine learning is to be used
 Offers the starting point for researchers in the rapidly growing field of physics and machine learning
Buy this book
 About this book

What is deep learning for those who study physics? Is it completely different from physics? Or is it similar?
In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics?
This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics.
In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically.
This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks.
We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored.  About the authors

Akinori Tanaka, Akio Tomiya, Koji Hashimoto
 Table of contents (13 chapters)


Forewords: Machine Learning and Physics
Pages 111

Introduction to Machine Learning
Pages 1734

Basics of Neural Networks
Pages 3555

Advanced Neural Networks
Pages 5775

Sampling
Pages 77102

Table of contents (13 chapters)
Recommended for you
Bibliographic Information
 Bibliographic Information

 Book Title
 Deep Learning and Physics
 Authors

 Akinori Tanaka
 Akio Tomiya
 Koji Hashimoto
 Series Title
 Mathematical Physics Studies
 Copyright
 2021
 Publisher
 Springer Singapore
 Copyright Holder
 The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
 eBook ISBN
 9789813361089
 DOI
 10.1007/9789813361089
 Hardcover ISBN
 9789813361072
 Series ISSN
 09213767
 Edition Number
 1
 Number of Pages
 XIII, 207
 Number of Illustrations
 17 b/w illustrations, 29 illustrations in colour
 Topics