Deep Learning Physics by Tanaka Akinori (34 results)
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Published by Springer, 2022
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Published by Springer, 2022
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Published by Springer 2022-02, 2022
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Taschenbuch. Condition: Neu. Neuware -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 Applic…ations 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.
Language: English
Published by Springer, 2022
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Language: English
Published by Springer, 2021
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Published by Springer, 2021
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Published by Springer, 2022
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Taschenbuch. Condition: Neu. Deep Learning and Physics | Akinori Tanaka (u. a.) | Taschenbuch | Mathematical Physics Studies | xiii | Englisch | 2022 | Springer | EAN 9789813361102 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter:…preigu.
Language: English
Published by Springer, Springer Feb 2022, 2022
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Taschenbuch. Condition: Neu. 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 kn…owing 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.
Language: English
Published by Springer, 2021
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Language: English
Published by Springer Nature, 2021
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Published by Springer, Springer, 2021
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Buch. Condition: 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 i…n 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.
Language: English
Published by Springer, Springer Feb 2022, 2022
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Taschenbuch. Condition: Neu. Neuware -What is deep learning for those who study physics Is it completely different from physics Or is it similar 224 pp. Englisch.
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Published by Springer, 2021
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Published by Springer, 2022
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Published by Springer, Springer Feb 2022, 2022
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Taschenbuch. Condition: 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 k…nowing 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.
Language: English
Published by Springer, 2021
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Published by Springer, Springer Feb 2022, 2022
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Taschenbuch. Condition: 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 k…nowing 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.
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Published by Springer Nature, 2022
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Published by Springer, 2022
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Published by Springer, Berlin|Springer Nature Singapore|Springer, 2022
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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.What is deep learning for those who study physics? Is it… completely different from physics.
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Published by Springer, 2022
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Published by Springer Nature Singapore, Springer Nature Singapore Feb 2022, 2022
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -What is deep learning for those who study physics Is it completely different from physics Or is it similar Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 224 pp. Englisch.
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Buch. Condition: 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.
