Introduction to machine learning / (Record no. 7034)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02793cam a2200349 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 21052111 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240702060711.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190703s2020 maua b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER | |
| LC control number | 2019028373 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780262043793 |
| Qualifying information | (hardcover) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9780262358064 |
| Qualifying information | (ebook) |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | DLC |
| Language of cataloging | eng |
| Transcribing agency | DLC |
| Description conventions | rda |
| Modifying agency | DLC |
| 042 ## - AUTHENTICATION CODE | |
| Authentication code | pcc |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | Q325.5 |
| Item number | .A46 2020 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 A46 2020 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Alpaydin, Ethem, |
| Relator term | author. |
| 245 10 - TITLE STATEMENT | |
| Title | Introduction to machine learning / |
| Statement of responsibility, etc. | Ethem Alpaydin. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | Fourth edition. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Cambridge, Massachusetts : |
| Name of producer, publisher, distributor, manufacturer | The MIT Press, |
| Date of production, publication, distribution, manufacture, or copyright notice | [2020] |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxiv, 682 pages : |
| Other physical details | illustrations ; |
| Dimensions | 24 cm. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | unmediated |
| Media type code | n |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Carrier type code | nc |
| Source | rdacarrier |
| 490 0# - SERIES STATEMENT | |
| Series statement | Adaptive computation and machine learning series |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | "Since the third edition of this text appeared in 2014, most recent advances in machine learning, both in theory and application, are related to neural networks and deep learning. In this new edition, the author has extended the discussion of multilayer perceptrons. He has also added a new chapter on deep learning including training deep neural networks, regularizing them so they learn better, structuring them to improve learning, e.g., through convolutional layers, and their recurrent extensions with short-term memory necessary for learning sequences. There is a new section on generative adversarial networks that have found an impressive array of applications in recent years. Alpaydin has also extended the chapter on reinforcement learning to discuss the use of deep networks in reinforcement learning. There is a new section on the policy gradient method that has been used frequently in recent years with neural networks, and two additional sections on two examples of deep reinforcement learning, which both made headlines when they were announced in 2015 and 2016 respectively. One is a network that learns to play arcade video games, and the other one learns to play Go. There are also revisions in other chapters reflecting new approaches, such as embedding methods for dimensionality reduction, and multi-label classification. In response to requests from instructors, this new edition contains two new appendices on linear algebra and optimization, to remind the reader of the basics of those topics that find use in machine learning"-- |
| Assigning source | Provided by publisher. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning. |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) | |
| a | 7 |
| b | cbc |
| c | orignew |
| d | 1 |
| e | ecip |
| f | 20 |
| g | y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | English Book |
| Suppress in OPAC | No |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type | Copy number |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | CENTRAL | CENTRAL | 07/17/2024 | 006.31 A46 2020 | 1000000015946 | 07/17/2024 | 07/17/2024 | English Book | ||||||
| Dewey Decimal Classification | WOMEN | WOMEN | 07/18/2024 | 006.31 A46 2020 | 1000000017904 | 07/18/2024 | 07/18/2024 | English Book | C2 |