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

Deep learning foundations

Jo, Taeho.

Linked to FindBook

Google Book

Amazon

博客來

Deep learning foundations

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning foundations/ by Taeho Jo.
Author:	Jo, Taeho.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xx, 426 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Part I. Foundation -- Supervised Learning -- Unsupervised Learning -- Ensemble Learning -- Part II. Deep Machine Learning -- Deep K Nearest Neighbor -- Deep Probabilistic Learning -- Deep Decision Tree -- Deep SVM -- Part III. Deep Neural Networks -- Multiple Layer Perceptron -- Recurrent Networks -- Restricted Boltzmann Machine -- Convolutionary Neural Networks -- Part IV. Textual Deep Learning -- Index Expansion -- Text Summarization -- Textual Deep Operations -- Convolutionary Text Classifier -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning) -
Online resource:	https://doi.org/10.1007/978-3-031-32879-4
ISBN:	9783031328794

Deep learning foundations
Jo, Taeho.

Deep learning foundations[electronic resource] /by Taeho Jo. - Cham :Springer International Publishing :2023. - xx, 426 p. :ill., digital ;24 cm.

Introduction -- Part I. Foundation -- Supervised Learning -- Unsupervised Learning -- Ensemble Learning -- Part II. Deep Machine Learning -- Deep K Nearest Neighbor -- Deep Probabilistic Learning -- Deep Decision Tree -- Deep SVM -- Part III. Deep Neural Networks -- Multiple Layer Perceptron -- Recurrent Networks -- Restricted Boltzmann Machine -- Convolutionary Neural Networks -- Part IV. Textual Deep Learning -- Index Expansion -- Text Summarization -- Textual Deep Operations -- Convolutionary Text Classifier -- Conclusion.

This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book's third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning. Provides a conceptual understanding of deep learning algorithms; Presents ways of modifying existing machine learning algorithms into deep learning algorithms for further analysis; Details how deep learning can solve problems such as classification, regression, and clustering.

ISBN: 9783031328794

Standard No.: 10.1007/978-3-031-32879-4doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

Deep learning foundations
LDR:02569nmm a2200325 a 4500 001 2333041
003 DE-He213
005 20230725052027.0
006 m d
007 cr nn 008maaau
008 240402s2023 sz s 0 eng d
020 $a 9783031328794 $q (electronic bk.)
020 $a 9783031328787 $q (paper)
024 7 $a 10.1007/978-3-031-32879-4 $2 doi
035 $a 978-3-031-32879-4
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.73
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.73 $b .J62 2023
100 1 $a Jo, Taeho. $3 1923746
245 1 0 $a Deep learning foundations $h [electronic resource] / $c by Taeho Jo.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xx, 426 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Part I. Foundation -- Supervised Learning -- Unsupervised Learning -- Ensemble Learning -- Part II. Deep Machine Learning -- Deep K Nearest Neighbor -- Deep Probabilistic Learning -- Deep Decision Tree -- Deep SVM -- Part III. Deep Neural Networks -- Multiple Layer Perceptron -- Recurrent Networks -- Restricted Boltzmann Machine -- Convolutionary Neural Networks -- Part IV. Textual Deep Learning -- Index Expansion -- Text Summarization -- Textual Deep Operations -- Convolutionary Text Classifier -- Conclusion.
520 $a This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book's third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning. Provides a conceptual understanding of deep learning algorithms; Presents ways of modifying existing machine learning algorithms into deep learning algorithms for further analysis; Details how deep learning can solve problems such as classification, regression, and clustering.
650 0 $a Deep learning (Machine learning) $3 3538509
650 0 $a Computer algorithms. $3 523872
650 1 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Automated Pattern Recognition. $3 3538549
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 1619875
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-32879-4
950 $a Engineering (SpringerNature-11647)