Principles of deep learning theory (Record no. 567271)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01880 a2200253 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20241125170846.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 241125b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781316519332 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | IIT Kanpur |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | R541p |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Roberts, Daniel A |
| 245 ## - TITLE STATEMENT | |
| Title | Principles of deep learning theory |
| Remainder of title | an effective theory approach to understanding neural networks |
| Statement of responsibility, etc | Daniel A. Roberts and Sho Yaida |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher | Cambridge University Press |
| Year of publication | 2022 |
| Place of publication | Cambridge |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | x, 460p |
| 500 ## - GENERAL NOTE | |
| General note | based on research in collaboration with Boris Hanin |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first-principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self-contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus, and informal probability theory, and it can easily fill a semester-long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Deep learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Mathematical & Computational |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Yaida, Sho |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Cost, replacement price | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| General Stacks | PK Kelkar Library, IIT Kanpur | PK Kelkar Library, IIT Kanpur | 22/11/2024 | 2 | 5230.00 | 006.31 R541p | A186619 | 6537.71 | Books |