Handbook of Deep Learning Applications (Hardcover, 1st ed. 2019)

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain-computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Smart Innovation, Systems and Technologies, 136
Release date:	March 2019
First published:	2019
Editors:	Valentina Emilia Balas • Sanjiban Sekhar Roy • Dharmendra Sharma • Pijush Samui
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	383
Edition:	1st ed. 2019
ISBN-13:	978-3-03-011478-7
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General
LSN:	3-03-011478-3
Barcode:	9783030114787

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