Handbook of Deep Learning Applications (Hardcover)

Valentina Emilia Balas

ISBN 10: 3030114783 / ISBN 13: 9783030114787

Editore: Springer Nature Switzerland AG, Cham, 2019

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Hardcover. 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, braincomputer 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Codice articolo 9783030114787

Dati bibliografici

• Titolo: Handbook of Deep Learning Applications (Hardcover)
• Editore: Springer Nature Switzerland AG, Cham
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3030114783
• ISBN 13: 9783030114787
• Rilegatura: Hardcover
• Condizione: new

Descrizione articolo

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, braincomputer 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.