Handbook of Deep Learning Applications

Valentina Emilia Balas, Sanjiban Sekhar Roy, Dharmendra Sharma, Pijush Samui

Research output: Book/ReportEdited Book

Abstract

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.
Original languageEnglish
PublisherSpringer - Adis
Number of pages383
Volume136
Edition1
ISBN (Electronic)9783030114794
ISBN (Print)9783030114787
DOIs
Publication statusPublished - 2019

Publication series

NameSmart Innovation, Systems and Technologies
PublisherSpringer
Volume136

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  • Cite this

    Balas, V. E., Roy, S. S., Sharma, D., & Samui, P. (2019). Handbook of Deep Learning Applications. (1 ed.) (Smart Innovation, Systems and Technologies; Vol. 136). Springer - Adis. https://doi.org/10.1007/978-3-030-11479-4