Handbook of Deep Learning Applications / / edited by Valentina Emilia Balas, Sanjiban Sekhar Roy, Dharmendra Sharma, Pijush Samui

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
• Edizione: 1st ed. 2019.
• Descrizione fisica: 1 online resource (380 pages)
• Disciplina: 006.3; 006.31
• Soggetto topico: Computational intelligence; Artificial intelligence; Signal processing; Image processing; Speech processing systems; Neural networks (Computer science) ; Data mining
• Persona (resp. second.): BalasValentina Emilia; RoySanjiban Sekhar; SharmaDharmendra; SamuiPijush
• Sommario/riassunto: 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.
• Titolo autorizzato: Handbook of Deep Learning Applications
• ISBN: 3-030-11479-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910484552903321
• Lo trovi qui: Univ. Federico II
• Serie: Smart Innovation, Systems and Technologies, . 2190-3018 ; ; 136