Deep learning and physics (Record no. 36769)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02790 a2200313 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NISER |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260119100614.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 260116b |||||||| |||| 00| 0 hin d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789813361102 |
| Qualifying information | Paperback |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | NISER LIBRARY |
| Language of cataloging | eng |
| Transcribing agency | NISER LIBRARY |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 530.1:004.85 |
| Item number | TAN-D |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Tanaka, Akinori |
| 245 10 - TITLE STATEMENT | |
| Title | Deep learning and physics |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | Singapore : |
| Name of publisher, distributor, etc. | Springer, |
| Date of publication, distribution, etc. | 2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xiii, 207 pages ; |
| Other physical details | illustrations (17 b/w illustrations, 29 illustrations in colour) ; |
| Dimensions | 24 cm. |
| 490 ## - SERIES STATEMENT | |
| Series statement | Mathematical physics studies |
| International Standard Serial Number | 0921-3767 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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/><br/><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/><br/><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 providesprogress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically. <br/><br/><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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Deep learning |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Mathematical physics |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Physics |
| General subdivision | Data processing |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Neural network |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Tomiya, Akio |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Hashimoto, Koji |
| 856 41 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Table of contents |
| Uniform Resource Identifier | <a href="https://link.springer.com/content/pdf/bfm:978-981-33-6108-9/1">https://link.springer.com/content/pdf/bfm:978-981-33-6108-9/1</a> |
| 856 41 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Reviews |
| Uniform Resource Identifier | <a href="https://www.goodreads.com/book/show/70812722-deep-learning-and-physics?ref=nav_sb_ss_1_13#CommunityReviews">https://www.goodreads.com/book/show/70812722-deep-learning-and-physics?ref=nav_sb_ss_1_13#CommunityReviews</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Book |
| Source of classification or shelving scheme | Universal Decimal Classification |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Full call number | Barcode | Date last seen | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Universal Decimal Classification | NISER LIBRARY | NISER LIBRARY | 15/01/2026 | 12 | 6347.20 | 530.1:004.85 TAN-D | 26388 | 15/01/2026 | 8462.94 | 15/01/2026 | Book |
