Dental Diagnostic Imaging: Diagnostic Features and Pathology

Medical Books

Langlais, Robert P.

Hashimoto, Koji

Yamamoto, Hirotsugu

Dentistry

Charles C. Thomas Pub Ltd

D-Brane: Superstrings and New Perspective of Our World

<p>Superstring theory is a promising theory which can potentially unify all the forces and the matters in particle physics. A new multi-dimensional object which is called "D-brane" was found. It drastically changed our perspective of a unified world. We may live on membrane-like hypersurfaces in higher dimensions ("braneworld scenario"), or we can create blackholes at particle accelarators, or the dynamics of quarks is shown to be equivalent to the higher dimensional gravity theory. All these scenarios are explained in this book with plain words but with little use of equations and with many figures. The book starts with a summary of long-standing problems in elementary particle physics and explains the D-branes and many applications of them. It ends with future roads for a unified ultimate theory of our world.</p>

Science & Math

Physics

Springer

Opening with a summary of long-standing problems in elementary particle physics, this book explains the newly discovered multi-dimensional objects called D-branes, describes their application and projects future roads for unified ultimate theory of our world.

Global Carbon Dioxide Recycling: For Global Sustainable Development by Renewable Energy (SpringerBriefs in Energy)

Business & Money

Economics

Deep Learning and Physics (Mathematical Physics Studies)

<p>What is deep learning for those who study physics? Is it completely different from physics? Or is it similar?&nbsp; <div><br /> <div>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?&nbsp;</div> <div><br /></div> <div>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.</div> <div><br /></div> <div>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.&nbsp;</div> <div><br /></div> <div>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.</div> <div><br /></div> <div>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.</div> <div><br /></div> </div></p>

Computers & Technology

Tanaka, Akinori

Tomiya, Akio

Computer Science

<p>What is deep learning for those who study physics? Is it completely different from physics? Or is it similar?<br />
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?<br />
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.<br />
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.<br />
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.<br />
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.\nFrom the Back Cover\nWhat is deep learning for those who study physics? Is it completely different from physics? Or is it similar?<br />
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?<br />
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.<br />
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.<br />
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.<br />
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.</p>

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Books by Hashimoto, Koji