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Advances in deep learning

SpringerLink (Online service)

Advances in deep learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advances in deep learningby M. Arif Wani ... [et al.].
其他作者:	Wani, M. Arif.
出版者:	Singapore :Springer Singapore :2020.
面頁冊數:	xiv, 149 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	EducationData processing.
電子資源:	https://doi.org/10.1007/978-981-13-6794-6
ISBN:	9789811367946$q(electronic bk.)

Advances in deep learning
Advances in deep learning[electronic resource] /by M. Arif Wani ... [et al.]. - Singapore :Springer Singapore :2020. - xiv, 149 p. :ill., digital ;24 cm. - Studies in big data,v.572197-6503 ;. - Studies in big data ;v.1..

Preface -- Introduction to Deep Learning -- Basic Deep Learning Models -- Training Basic Deep Learning Models -- Optimising Deep Learning Models -- Application of Deep Learning in Classification -- Application of Deep Learning in Segmentation -- Application of Deep Learning in Face Recognition -- Application of Deep Learning in Fingerprint Recognition -- Author's Index.

This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.

ISBN: 9789811367946$q(electronic bk.)

Standard No.: 10.1007/978-981-13-6794-6doiSubjects--Topical Terms:

207353
Education
--Data processing.

LC Class. No.: LB1065

Dewey Class. No.: 370.1523

Advances in deep learning
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520 $a This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.
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