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portada Multi-faceted Deep Learning
Type
Physical Book
Publisher
Author
Year
2021
Language
English
Pages
330
Format
Paperback
Dimensions
23.4x15.6 cm
ISBN13
9783030744793

Multi-faceted Deep Learning

Akka Zemmari;Jenny Benois-Pineau (Author) · Springer Nature B.V. · Paperback

Multi-faceted Deep Learning - Akka Zemmari;Jenny Benois-Pineau

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Synopsis "Multi-faceted Deep Learning"

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.

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