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

Shallow and deep learning principles...

Sen, Zekai.

Linked to FindBook

Google Book

Amazon

博客來

Shallow and deep learning principles = scientific, philosophical, and logical perspectives /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Shallow and deep learning principles/ by Zekai Sen.
Reminder of title:	scientific, philosophical, and logical perspectives /
Author:	Sen, Zekai.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xx, 661 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Philosophical and Logical Principles in Science -- Uncertainty and Modeling Principles -- Mathematical Modeling Principles -- Genetic Algorithm -- Artificial Neural Networks -- Artıfıcıal Intellıgence -- Machıne Learnıng -- Deep Learning -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science) -
Online resource:	https://doi.org/10.1007/978-3-031-29555-3
ISBN:	9783031295553

Shallow and deep learning principles = scientific, philosophical, and logical perspectives /
Sen, Zekai.

Shallow and deep learning principlesscientific, philosophical, and logical perspectives /[electronic resource] :by Zekai Sen. - Cham :Springer International Publishing :2023. - xx, 661 p. :ill., digital ;24 cm.

Introduction -- Philosophical and Logical Principles in Science -- Uncertainty and Modeling Principles -- Mathematical Modeling Principles -- Genetic Algorithm -- Artificial Neural Networks -- Artıfıcıal Intellıgence -- Machıne Learnıng -- Deep Learning -- Conclusion.

This book discusses Artificial Neural Networks (ANN) and their ability to predict outcomes using deep and shallow learning principles. The author first describes ANN implementation, consisting of at least three layers that must be established together with cells, one of which is input, the other is output, and the third is a hidden (intermediate) layer. For this, the author states, it is necessary to develop an architecture that will not model mathematical rules but only the action and response variables that control the event and the reactions that may occur within it. The book explains the reasons and necessity of each ANN model, considering the similarity to the previous methods and the philosophical - logical rules.

ISBN: 9783031295553

Standard No.: 10.1007/978-3-031-29555-3doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Shallow and deep learning principles = scientific, philosophical, and logical perspectives /
LDR:01991nmm a2200325 a 4500 001 2332212
003 DE-He213
005 20230601200846.0
006 m d
007 cr nn 008maaau
008 240402s2023 sz s 0 eng d
020 $a 9783031295553 $q (electronic bk.)
020 $a 9783031295546 $q (paper)
024 7 $a 10.1007/978-3-031-29555-3 $2 doi
035 $a 978-3-031-29555-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .S474 2023
100 1 $a Sen, Zekai. $3 900219
245 1 0 $a Shallow and deep learning principles $h [electronic resource] : $b scientific, philosophical, and logical perspectives / $c by Zekai Sen.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xx, 661 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Philosophical and Logical Principles in Science -- Uncertainty and Modeling Principles -- Mathematical Modeling Principles -- Genetic Algorithm -- Artificial Neural Networks -- Artıfıcıal Intellıgence -- Machıne Learnıng -- Deep Learning -- Conclusion.
520 $a This book discusses Artificial Neural Networks (ANN) and their ability to predict outcomes using deep and shallow learning principles. The author first describes ANN implementation, consisting of at least three layers that must be established together with cells, one of which is input, the other is output, and the third is a hidden (intermediate) layer. For this, the author states, it is necessary to develop an architecture that will not model mathematical rules but only the action and response variables that control the event and the reactions that may occur within it. The book explains the reasons and necessity of each ANN model, considering the similarity to the previous methods and the philosophical - logical rules.
650 0 $a Neural networks (Computer science) $3 532070
650 0 $a Machine learning. $3 533906
650 1 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Probability and Statistics in Computer Science. $3 891072
650 2 4 $a Innovation and Technology Management. $3 3538485
650 2 4 $a Artificial Intelligence. $3 769149
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-29555-3
950 $a Engineering (SpringerNature-11647)