Multi-faceted Deep Learning - Models and Data (Hardcover, 1st ed. 2021)

This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem-oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Release date:	October 2021
First published:	2021
Editors:	Jenny Benois-Pineau • Akka Zemmari
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	316
Edition:	1st ed. 2021
ISBN-13:	978-3-03-074477-9
Categories:	Books > Computing & IT > Computer software packages > Multimedia Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Image processing > General
LSN:	3-03-074477-9
Barcode:	9783030744779

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