Handbook of Deep Learning Applications

Valentina Emilia Balas

ISBN 10: 3030114783 / ISBN 13: 9783030114787

Verlag: Springer International Publishing, Springer Nature Switzerland Mär 2019, 2019

Beschreibung

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 392 pp. Englisch.

Bestandsnummer des Verkäufers 9783030114787

Bibliografische Details

• Titel: Handbook of Deep Learning Applications
• Verlag: Springer International Publishing, Springer Nature Switzerland Mär 2019
• Erscheinungsjahr: 2019
• Sprache: Englisch
• ISBN-10: 3030114783
• ISBN-13: 9783030114787
• Einband: Buch
• Zustand: Neu
• Abmessungen: 241B x 160 H x 27 T Millimeter Breite: 241 mm, Höhe: 160 mm, Tiefe: 27 mm
• Gewicht: 752 Gramm

Über diesen Titel

Inhaltsangabe

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.