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Introduction to machine learning /

Alpaydin, Ethem.

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Introduction to machine learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Introduction to machine learning // Ethem Alpaydin.
作者:	Alpaydin, Ethem.
出版者:	Cambridge, Mass. :The MIT Press, : 2020.,
面頁冊數:	xxiv, 682 p. :ill. ;24 cm.
標題:	Machine learning. -
ISBN:	9780262043793 (hbk.) :

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: 9780262043793 (hbk.) :NT1490.00

LCCN: 2019028373Subjects--Topical Terms:

533906
Machine learning.

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

Dewey Class. No.: 006.31

Introduction to machine learning /
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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.
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