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Advances in deep learning.. Volume 2

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Advances in deep learning./ by M. Arif Wani ... [et al.].
other author:	Wani, M. Arif.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xvi, 186 p. :ill. (chiefly color), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning) -
Online resource:	https://doi.org/10.1007/978-981-96-3498-9
ISBN:	9789819634989

Advances in deep learning.. Volume 2
Advances in deep learning.Volume 2[electronic resource] /by M. Arif Wani ... [et al.]. - Singapore :Springer Nature Singapore :2025. - xvi, 186 p. :ill. (chiefly color), digital ;24 cm. - Studies in big data,v. 122197-6511 ;. - Studies in big data ;v.1..

Introduction to Deep Learning Architectures -- Evolutionary Algorithm-based Neural Architecture SearchEvolutionary Algorithm-based Neural Architecture Search -- Gradient-based Neural Architecture Search.

This book describes novel ways of using deep learning to solve real-world problems. It covers advanced deep learning topics like neural architecture search, ensemble deep learning, transfer learning techniques, lightweight architectures, hybrid deep learning approaches, and generative adversarial networks. The book discusses the use of these advanced topics in selected applications like image classification, object detection, image steganography, protein secondary structure prediction, and gene expression data classification. Various challenges and future research directions falling under the scope of these topics are discussed.

ISBN: 9789819634989

Standard No.: 10.1007/978-981-96-3498-9doiSubjects--Topical Terms:

1381171
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

Advances in deep learning.. Volume 2
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505 0 $a Introduction to Deep Learning Architectures -- Evolutionary Algorithm-based Neural Architecture SearchEvolutionary Algorithm-based Neural Architecture Search -- Gradient-based Neural Architecture Search.
520 $a This book describes novel ways of using deep learning to solve real-world problems. It covers advanced deep learning topics like neural architecture search, ensemble deep learning, transfer learning techniques, lightweight architectures, hybrid deep learning approaches, and generative adversarial networks. The book discusses the use of these advanced topics in selected applications like image classification, object detection, image steganography, protein secondary structure prediction, and gene expression data classification. Various challenges and future research directions falling under the scope of these topics are discussed.
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