東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Introduction to machine learning /

Alpaydin, Ethem.

Linked to FindBook

Google Book

Amazon

博客來

Introduction to machine learning /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Introduction to machine learning // Ethem Alpaydin.
Author:	Alpaydin, Ethem.
Published:	Cambridge, Mass. :The MIT Press, : 2020.,
Description:	xxiv, 682 p. :ill. ;24 cm.
Subject:	Machine learning. -
ISBN:	9780262043793

Introduction to machine learning /
Alpaydin, Ethem.

Introduction to machine learning /Ethem Alpaydin. - 4th ed. - Cambridge, Mass. :The MIT Press,2020. - xxiv, 682 p. :ill. ;24 cm. - Adaptive computation and machine learning series. - Adaptive computation and machine learning series..

Includes bibliographical references and index.

"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"--

ISBN: 9780262043793NT1490.00

LCCN: 2019028373Subjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .A46 2020

Dewey Class. No.: 006.31

Introduction to machine learning /
LDR:02263cam a2200229 a 4500 001 2339514
005 20220418151259.0
008 240612s2020 maua b 001 0 eng d
010 $a 2019028373
020 $a 9780262043793 $q (hbk.) : $c NT1490.00
020 $z 9780262358064 $q (ebk.)
040 $a NTUT $b eng $d NDHU
041 0 # $a eng
042 $a nbic
050 0 0 $a Q325.5 $b .A46 2020
082 0 4 $a 006.31 $2 23
100 1 $a Alpaydin, Ethem. $3 581976
245 1 0 $a Introduction to machine learning / $c Ethem Alpaydin.
250 $a 4th ed.
260 # $a Cambridge, Mass. : $b The MIT Press, $c 2020.
300 $a xxiv, 682 p. : $b ill. ; $c 24 cm.
490 1 $a Adaptive computation and machine learning series
504 $a Includes bibliographical references and index.
520 # $a "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"-- $c Provided by publisher.
650 # 0 $a Machine learning. $3 533906
830 0 $a Adaptive computation and machine learning series. $3 3692786