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Deep Learning and Physics
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 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.
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 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.
- GenresPhysics
220 pages, Hardcover
Published February 21, 2021
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Displaying 1 of 1 review
February 28, 2021
私はすでに本社の日本語版を読んでいます｡
本書は人工知能によってできる物理学的な計算方法を提示しています｡
その中で特に重要なのか逆数学です｡
これによって人工知能はまだ人類が発見してない物理学法則を見つけ出す可能性があります❗❗
本書は人工知能によってできる物理学的な計算方法を提示しています｡
その中で特に重要なのか逆数学です｡
これによって人工知能はまだ人類が発見してない物理学法則を見つけ出す可能性があります❗❗
Displaying 1 of 1 review