Téléchargez le livre : Multi-faceted Deep Learning

Multi-faceted Deep Learning

Models and Data de

,

Éditeur :

Springer


Paru le : 2021-10-20

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Description

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.
Pages
316 pages
Collection
n.c
Parution
2021-10-20
Marque
Springer
EAN papier
9783030744779
EAN PDF
9783030744786
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
31
Taille du fichier
7270 Ko
Prix
179,34 €
EAN EPUB
9783030744786
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
31
Taille du fichier
21248 Ko
Prix
179,34 €

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