語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
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,
面頁冊數:
xxiv, 682 pages :illustrations ;24 cm.
標題:
Machine learning.
ISBN:
9780262043793
Introduction to machine learning /
Alpaydin, Ethem,
Introduction to machine learning /
Ethem Alpaydin. - Fourth edition. - xxiv, 682 pages :illustrations ;24 cm. - Adaptive computation and machine learning. - Adaptive computation and machine learning..
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
LCCN: 2019028373
Nat. Bib. No.: GBC015585bnb
Nat. Bib. Agency Control No.: 019699500UkSubjects--Topical Terms:
188639
Machine learning.
LC Class. No.: Q325.5 / .A46 2020
Dewey Class. No.: 006.3/1
Introduction to machine learning /
LDR
:02935cam 2200433 i 4500
001
637372
003
OCoLC
005
20230920210103.0
008
230921s2020 maua b 001 0 eng
010
$a
2019028373
015
$a
GBC015585
$2
bnb
016
7
$a
019699500
$2
Uk
019
$a
1374229166
020
$a
9780262043793
$q
hardcover
020
$a
0262043793
$q
hardcover
020
$z
9780262358064
$q
electronic book
029
1
$a
UKMGB
$b
019699500
029
1
$a
AU@
$b
000065836659
029
1
$a
CHVBK
$b
589492438
029
1
$a
CHBIS
$b
011457018
029
1
$a
CHVBK
$b
58698769X
029
1
$a
CHDSB
$b
007185242
035
$a
(OCoLC)1108782604
$z
(OCoLC)1374229166
035
$a
on1108782604
040
$a
DLC
$b
eng
$e
rda
$c
DLC
$d
OCLCO
$d
OCLCF
$d
UKMGB
$d
YDX
$d
CHVBK
$d
OCLCO
$d
UKOBU
$d
OCLCO
$d
VNVGU
042
$a
pcc
049
$a
NUKM
050
0 0
$a
Q325.5
$b
.A46 2020
082
0 0
$a
006.3/1
$2
23
100
1
$a
Alpaydin, Ethem,
$e
author.
$3
943759
245
1 0
$a
Introduction to machine learning /
$c
Ethem Alpaydin.
250
$a
Fourth edition.
264
1
$a
Cambridge, Massachusetts :
$b
The MIT Press,
$c
[2020]
300
$a
xxiv, 682 pages :
$b
illustrations ;
$c
24 cm.
336
$a
text
$b
txt
$2
rdacontent
337
$a
unmediated
$b
n
$2
rdamedia
338
$a
volume
$b
nc
$2
rdacarrier
490
0
$a
Adaptive computation and machine learning
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
188639
650
6
$a
Apprentissage automatique.
$3
238808
650
7
$a
Maschinelles Lernen
$2
gnd
$3
943760
830
0
$a
Adaptive computation and machine learning.
$3
486977
994
$a
C0
$b
TWNUK
筆 0 讀者評論
全部
西方語文圖書區(四樓)
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
320000742082
西方語文圖書區(四樓)
1圖書
一般圖書
Q325.5 A456 2020
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入