Deep Learning in Visual Computing

Deep Learning in Visual Computing

EnglishPaperback / softbackPrint on demand
Ugail Hassan
Taylor & Francis Ltd
EAN: 9780367549633
Print on demand
Delivery on Friday, 7. of August 2026
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Detailed information

Deep learning is an artificially intelligent entity that teaches itself and can be utilized to make predictions. Deep learning mimics the human brain and provides learned solutions addressing many challenging problems in the area of visual computing. From object recognition to image classification for diagnostics, deep learning has shown the power of artificial deep neural networks in solving real world visual computing problems with super-human accuracy. The introduction of deep learning into the field of visual computing has meant to be the death of most of the traditional image processing and computer vision techniques. Today, deep learning is considered to be the most powerful, accurate, efficient and effective method with the potential to solve many of the most challenging problems in visual computing.

This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine learning. It introduces readers to the world of deep neural network architectures with easy-to-understand explanations. From face recognition to image classification for diagnosis of cancer, the book provides unique examples of solved problems in applied visual computing using deep learning. Interested and enthusiastic readers of modern machine learning methods will find this book easy to follow. They will find it a handy guide for designing and implementing their own projects in the field of visual computing.

EAN 9780367549633
ISBN 0367549638
Binding Paperback / softback
Publisher Taylor & Francis Ltd
Publication date July 7, 2022
Pages 134
Language English
Dimensions 216 x 138
Country United Kingdom
Authors Ugail Hassan
Illustrations 32 Tables, black and white; 20 Line drawings, black and white; 13 Halftones, black and white; 33 Illustrations, black and white
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