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Book synopsis

Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models. Provides insights into the theory, algorithms, implementation and the application of deep learning techniquesCovers a wide range of applications of deep learning across smart healthcare and smart engineeringInvestigates the development of new models and how they can be exploited to find appropriate solutions

Book details

• Full title: Trends in Deep Learning Methodologies
• Author: Sandeep Raj, Angelo Genovese
• Language: English
• Binding: Paperback
• Number of pages: 306
• EAN: 9780128222263
• ISBN: 0128222263
• ID: 33011977
• Publisher: Elsevier Science Publishing Co Inc
• Weight: 496 g
• Dimensions: 228 × 152 × 20 mm
• Date of publishing: 16. November 2020

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