Cantitate/Preț
Produs

Deep Learning and Physics (Mathematical Physics Studies)

De (autor) , ,
Notă GoodReads:
en Limba Engleză Paperback – 22 Feb 2022
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.

Citește tot Restrânge
Toate formatele și edițiile
Toate formatele și edițiile Preț Express
Paperback (1) 39817 lei  7-9 săpt. +22614 lei  12-20 zile
  Springer Nature Singapore – 22 Feb 2022 39817 lei  7-9 săpt. +22614 lei  12-20 zile
Hardback (1) 54385 lei  7-9 săpt. +32090 lei  12-20 zile
  Springer Nature Singapore – 21 Feb 2021 54385 lei  7-9 săpt. +32090 lei  12-20 zile

Din seria Mathematical Physics Studies

Preț: 39817 lei

Preț vechi: 42359 lei
-6%

Puncte Express: 597

Preț estimativ în valută:
7731 7916$ 6528£

Carte tipărită la comandă

Livrare economică 29 septembrie-13 octombrie
Livrare express 23-31 august pentru 23613 lei

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9789813361102
ISBN-10: 9813361107
Ilustrații: XIII, 207 p. 46 illus., 29 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.32 kg
Ediția: 1st ed. 2021
Editura: Springer Nature Singapore
Colecția Springer
Seria Mathematical Physics Studies

Locul publicării: Singapore, Singapore

Cuprins

Chapter 1: Forewords: Machine learning and physics.- Part I Physical view of deep learning.- Chapter 2: Introduction to machine learning.- Chapter 3: Basics of neural networks.- Chapter 4: Advanced neural networks.- Chapter 5: Sampling.- Chapter 6: Unsupervised deep learning.- Part II Applications to physics.- Chapter 7: Inverse problems in physics.- Chapter 8: Detection of phase transition by machines.- Chapter 9: Dynamical systems and neural networks.- Chapter 10: Spinglass and neural networks.- Chapter 11: Quantum manybody systems, tensor networks and neural networks.- Chapter 12: Application to superstring theory.- Chapter 13: Epilogue.- Bibliography.- Index.

Notă biografică

Akinori Tanaka, Akio Tomiya, Koji Hashimoto

Textul de pe ultima copertă

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.

Caracteristici

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