9789811536847 - deep neural evolution: deep learning with evolutionary computation (14 risultati)
Lingua: Inglese
Editore: Springer 2020
Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Editore: Springer 2020
Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Lingua: Inglese
Editore: Springer 2020
Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Springer 2020
Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Lingua: Inglese
Editore: Springer 2020
Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condizione: New.
Lingua: Inglese
Editore: Springer Nature Singapore, Springer Nature Singapore 2020
Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and automatic game play…ing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data. Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL.EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes. Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research -from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN).This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice.
Lingua: Inglese
Editore: Springer-Nature New York Inc 2020
Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Da: Revaluation Books, Exeter, , Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 450 pages. 9.25x6.10x9.21 inches. In Stock.
Lingua: Inglese
Editore: Springer 2020
Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer Nature Singapore 2020
Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Da: moluna, Greven, , Germaniamoluna
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EUR 180,07
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a compilation of state-of-the-art research in deep learning using evolutionary computationFeatures hyper-parameter optimization, deep neural network architecture design, and deep neuroevolution Facilitates r…esearch both in theory a.
Lingua: Inglese
Editore: Springer Nature Singapore, Springer Nature Singapore Mai 2020 2020
Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 213,99
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and aut…omatic game playing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data. Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL.EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes. Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research -from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN).This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice. 452 pp. Englisch.
Lingua: Inglese
Editore: Springer, Springer Mai 2020 2020
Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 213,99
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book delivers the state of the art in deep learning (DL) methods hybridized with evolutionary computation (EC). Over the last decade, DL has dramatically reformed many domains: computer vision, speech recognition, healthcare, and automat…ic game playing, to mention only a few. All DL models, using different architectures and algorithms, utilize multiple processing layers for extracting a hierarchy of abstractions of data. Their remarkable successes notwithstanding, these powerful models are facing many challenges, and this book presents the collaborative efforts by researchers in EC to solve some of the problems in DL.EC comprises optimization techniques that are useful when problems are complex or poorly understood, or insufficient information about the problem domain is available. This family of algorithms has proven effective in solving problems with challenging characteristics such as non-convexity, non-linearity, noise, and irregularity, which dampen the performance of most classic optimization schemes. Furthermore, EC has been extensively and successfully applied in artificial neural network (ANN) research ¿from parameter estimation to structure optimization. Consequently, EC researchers are enthusiastic about applying their arsenal for the design and optimization of deep neural networks (DNN).This book brings together the recent progress in DL research where the focus is particularly on three sub-domains that integrate EC with DL: (1) EC for hyper-parameter optimization in DNN; (2) EC for DNN architecture design; and (3) Deep neuroevolution. The book also presents interesting applications of DL with EC in real-world problems, e.g., malware classification and object detection. Additionally, it covers recent applications of EC in DL, e.g. generative adversarial networks (GAN) training and adversarial attacks. The book aims to prompt and facilitate the research in DL with EC both in theory and in practice.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 452 pp. Englisch.
Lingua: Inglese
Editore: Springer 2020
Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Da: Majestic Books, Hounslow, , Regno UnitoMajestic Books
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EUR 276,76
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Condizione: New. Print on Demand.
Lingua: Inglese
Editore: Springer 2020
Serie: Natural Computing, Libro 32 di 32. Libro 32 di 32 - Natural Computing
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 274,10
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Condizione: New. PRINT ON DEMAND.
