國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Shallow and deep learning principles/ by Zekai Sen.
其他題名:	scientific, philosophical, and logical perspectives /
作者:	Sen, Zekai.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xx, 661 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Neural networks (Computer science) -
電子資源:	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:

528588
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:01991nam a2200325 a 4500 001 1105865
003 DE-He213
005 20230601200846.0
006 m d
007 cr nn 008maaau
008 231013s2023 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 679510
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 : $c 2023. $b Springer International Publishing : $b Imprint: Springer,
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 528588
650 0 $a Machine learning. $3 561253
650 1 4 $a Communications Engineering, Networks. $3 669809
650 2 4 $a Probability and Statistics in Computer Science. $3 669886
650 2 4 $a Innovation and Technology Management. $3 1365778
650 2 4 $a Artificial Intelligence. $3 646849
710 2 $a SpringerLink (Online service) $3 593884
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)