Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Book Faculty Publications- Book Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Deep learning: Research and applications Description An account of the resource This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition. Tutorials on deep learning framework with focus on tensor flow, keras etc. Numerous worked out examples on real life applications Illustrative diagrams and coding examples. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. Creator An entity primarily responsible for making the resource Bhattacharyya S.; Snasel V.; Hassanien A.E.; Saha S.; Tripathy B.K. Source A related resource from which the described resource is derived Deep Learning: Research and Applications, pp. 1-156. Publisher An entity responsible for making the resource available De Gruyter Mouton Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1515/9783110670905" target="_blank" rel="noreferrer noopener">https://doi.org/10.1515/9783110670905</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092121037&amp;doi=10.1515%2F9783110670905&amp;partnerID=40&amp;md5=ebe9036cf252e95b8b9f99fb507a807e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092121037&amp;doi=10.1515%2f9783110670905&amp;partnerID=40&amp;md5=ebe9036cf252e95b8b9f99fb507a807e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-311067090-5; 978-311067079-0 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bhattacharyya S., CHRIST (Deemed to be University), Bangalore, Karnataka, India; Snasel V., VSB Technical University of Ostrava, 17 Listopadu 2172/15, Ostrava, 708 00, Czech Republic; Hassanien A.E., Cairo University Information Technology, Department Ahmed Zewail, 12613 Giza Governorate, Ad Doqi, Dokki, Egypt; Saha S., MCKV Institute of Engineering, G T Road North 243, Liluah, West Bengal, 711204, India; Tripathy B.K., School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India