| 000 -LEADER |
|---|
| fixed length control field |
02280cam a2200229 i 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20230916122046.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
190703s2020 maua b 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9780262043793 |
|
|---|
| Cancelled/invalid ISBN |
9780262358064 |
| 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 |
| Edition number |
23 |
| Item number |
A456 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Alpaydin, Ethem, |
| Relator term |
author. |
| 9 (RLIN) |
45785 |
| 245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Title |
Introduction to machine learning / |
| Statement of responsibility, etc |
Ethem Alpaydin. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
4th ed.. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Cambridge, Massachusetts : |
| Name of publisher, distributor, etc |
The MIT Press, |
| Date of publication, distribution, etc |
2020. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxiv, 682 p. : |
| Other physical details |
ill. ; |
| Dimensions |
24 cm. |
| 490 0# - SERIES STATEMENT |
|---|
| Series statement |
Adaptive computation and machine learning series |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
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"-- |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Book |
