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Multi-faceted Deep Learning - Models and Data (Paperback, 1st ed. 2021)
Loot Price: R5,259
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Multi-faceted Deep Learning - Models and Data (Paperback, 1st ed. 2021)
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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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