Multi-faceted Deep Learning: Models and Data


Multi-faceted Deep Learning: Models and Data
Authors: Jenny Benois-Pineau (editor)
Year: 2021
Publisher: Springer
Language: English
ISBN 13: 9783030744779
ISBN 10: 3030744779
Categories: Computers, Computer Science
Pages: 320 / 319
Edition: 1st ed. 2021

Availability: 5000 in stock

SKU: 9783030744779 Categories: ,

Multi-faceted Deep Learning: Models and Data Jenny Benois-Pineau (editor), Akka Zemmari (editor)
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. Categories:
Computers – Computer Science
1st ed. 2021
328 / 321
ISBN 10:
ISBN 13:
98 MB


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Multi-faceted Deep Learning: Models and Data

Availability: 5000 in stock