 Название: Deep Learning in Mining of Visual Content
Автор: Akka Zemmari, Jenny Benois-Pineau
Издательство: Springer
Год: 2020
Страниц: 117
Язык: английский
Формат: pdf (true), djvu
Размер: 10.1 MB
This book provides the reader with the fundamental knowledge in the area of Deep Learning (DL) with application to visual content mining. The authors give a fresh view on Deep Learning approaches both from the point of view of image understanding and supervised Machine Learning (ML). It contains chapters which introduce theoretical and mathematical foundations of neural networks and related optimization methods. Then it discusses some particular very popular architectures used in the domain: convolutional neural networks and recurrent neural networks.
Deep Learning is currently at the heart of most cutting edge technologies. It is in the core of the recent advances in Artificial Intelligence. Visual information in Digital form is constantly growing in volume. In such active domains as Computer Vision and Robotics visual information understanding is based on the use of deep learning. Other chapters present applications of deep learning for visual content mining. These include attention mechanisms in deep neural networks and application to digital cultural content mining. An additional application field is also discussed, and illustrates how Deep Learning can be of very high interest to computer-aided diagnostics of Alzheimer’s disease on multimodal imaging.
The proliferation of software frameworks allows for easy design and implementation of deep architectures, for the choice and adequate parameterization of different optimization algorithms for training parameters of deep neural networks. The availability of graphical processing units (GPU) and of distributed computing made the computational times for learning quite reasonable. For young researchers and those who move to this kind of methods it is important, we think, to get very quickly into comprehension of underlying mathematical models and formalism, but also to make a bridge between the methods previously used for understanding of images and videos and these winning tools.
It is difficult today to write a book about deep learning, so numerous are different tutorials easily accessible on the Internet. What is the particularity of our book compared to them? We tried to keep a sufficient balance between the usage of mathematical formalism, graphical illustrations, and real-world examples. The book should be easy to understand for young researchers and professionals with engineering and computer science background. Deep learning without pain, this is our goal.
This book targets advanced-level students studying computer science including computer vision, data analytics and multimedia. Researchers and professionals working in computer science, signal and image processing may also be interested in this book.
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