Multi-faceted Deep Learning: Models and Data
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ISBN 10: 3030744779 ISBN 13: 9783030744779
Editorial: Springer, 2021
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Descripción
Descripción:
2021. Hardcover. . . . . . Books ship from the US and Ireland. N° de ref. del artículo V9783030744779
Sinopsis:
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.
Acerca del autor:
Prof. Jenny Benois-Pineau is a full professor of Computer Science at the University Bordeaux. Her topics of interest include image/multimedia, artificial intelligence in multimedia and healthcare. She is the author and co-author of more than 200 papers in international journals, conference proceedings, books and book chapters. She is associated editor of Eurasip SPIC, ACM MTAP, senior associated editor JEI SPIE journals. She has organized workshops and special sessions at international conferences IEEE ICIP, ACM MM,... She has served in numerous program committees in international conferences: ACM MM, ACM ICMR, ACM CIVR, CBMI, IPTA, ACM MMM. She has been coordinator or leading researcher in EU – funded and French national research projects. She is a member of IEEE TC IVMSP. She has Knight of Academic Palms grade.
Dr. Akka Zemmari has received his Ph.D. degree from the University of Bordeaux 1, France, in 2000. He is an associate professor in computer science since 2001 at University of Bordeaux, France. His research interests include Artificial Intelligence, Deep Learning, Distributed algorithms and systems, Graphs, Randomized Algorithms, and Security. He wrote one book and more than 80 research papers published in international journals and conference proceedings and he is involved in program committees and organization committees of international conferences.
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Título: Multi-faceted Deep Learning: Models and Data
Editorial: Springer
Año de publicación: 2021
Encuadernación: Encuadernación de tapa dura
Condición: New
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents high priority problems in the field of  Deep Learning, Multimedia, Visual Data Representation, Interpretation and Coding Covers low supervision and metric learningDiscusses cross-media and cross modality in decision . Nº de ref. del artículo: 458554092
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Hardcover. Condición: new. Hardcover. 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 problemoriented 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. This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783030744779
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Buch. Condición: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch. Nº de ref. del artículo: 9783030744779
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Multi-faceted Deep Learning : Models and Data
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783030744779
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