000			02790 a2200313 4500
003			NISER
005			20260119100614.0
008			260116b |||||||| |||| 00| 0 hin d
020			_a9789813361102 _qPaperback
040			_aNISER LIBRARY _beng _cNISER LIBRARY
082	0	4	_a530.1:004.85 _bTAN-D
100	1		_aTanaka, Akinori
245	1	0	_aDeep learning and physics
260			_aSingapore : _bSpringer, _c2021.
300			_axiii, 207 pages ; _billustrations (17 b/w illustrations, 29 illustrations in colour) ; _c24 cm.
490			_aMathematical physics studies _x0921-3767
504			_aIncludes bibliographical references and index.
520			_aWhat 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.
650		0	_aDeep learning
650		0	_aMachine learning
650		0	_aMathematical physics
650		0	_aPhysics _xData processing
650		0	_aNeural network
700	1		_aTomiya, Akio
700	1		_aHashimoto, Koji
856	4	1	_3Table of contents _uhttps://link.springer.com/content/pdf/bfm:978-981-33-6108-9/1
856	4	1	_3Reviews _uhttps://www.goodreads.com/book/show/70812722-deep-learning-and-physics?ref=nav_sb_ss_1_13#CommunityReviews
942			_cBK _2udc
999			_c36769 _d36769