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Shallow and deep learning principles...

Sen, Zekai.

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

紀錄類型:	書目-電子資源 : 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.
內容註:	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
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:

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 /
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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.
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