Deep learning: Research and applications

Title

Deep learning: Research and applications

Creator

Bhattacharyya S.; Snasel V.; Hassanien A.E.; Saha S.; Tripathy B.K.

Description

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.

Source

Deep Learning: Research and Applications, pp. 1-156.

Date

2020-01-01

Publisher

De Gruyter Mouton

Coverage

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

Rights

Restricted Access

Relation

ISBN: 978-311067090-5; 978-311067079-0

Format

Online

Language

English

Type

Book

Identifier

https://doi.org/10.1515/9783110670905

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