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Deep Learning and Physics (Mathematical Physics Studies) - Tapa dura
Libro 23 de 24: Mathematical Physics Studies
Sinopsis
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Akinori Tanaka, Akio Tomiya, Koji Hashimoto
De la contraportada
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.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Deep Learning and Physics (eng)
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Deep Learning and Physics (Mathematical Physics Studies) [Hardcover] [Feb 21, 2021] Tanaka, Akinori; Tomiya, Akio and Hashimoto, Koji
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Deep Learning and Physics
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Springer Nature Singapore Feb 2021, 2021
ISBN 10: 9813361077
ISBN 13: 9789813361072
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 providesprogress 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. 224 pp. Englisch. Nº de ref. del artículo: 9789813361072
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Is the first machine learning textbook written by physicists so that physicists and undergraduates can learn easilyPresents applications to physics problems written so that readers can soon imagine how machine learning is to be used . Nº de ref. del artículo: 418570588
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 providesprogress 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. Nº de ref. del artículo: 9789813361072
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