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Hardcover. Condition: Very Good. 1. Auflage. Unread, some shelfwear. Immediately dispatched from Germany.
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Evolutionary Approach to Machine Learning and Deep Neural Networks: Neuro-Evolution and Gene Regulatory Networks
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Evolutionary Approach to Machine Learning and Deep Neural Networks: Neuro-Evolution and Gene Regulatory Networks
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Evolutionary Approach to Machine Learning and Deep Neural Networks : Neuro-evolution and Gene Regulatory Networks
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Condition: New. Softcover reprint of the original 1st ed. 2018 edition NO-PA16APR2015-KAP.
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Evolutionary Approach to Machine Learning and Deep Neural Networks : Neuro-evolution and Gene Regulatory Networks
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Deep Neural Evolution: Deep Learning with Evolutionary Computation (Natural Computing Series)
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Deep Neural Evolution: Deep Learning with Evolutionary Computation (Natural Computing Series)
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Deep Neural Evolution : Deep Learning With Evolutionary Computation
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Evolutionary Approach to Machine Learning and Deep Neural Networks : Neuro-Evolution and Gene Regulatory Networks
Language: English
Published by Springer Nature Singapore, 2019
ISBN 10: 9811343586 ISBN 13: 9789811343582
Condition: Sehr gut. Zustand: Sehr gut | Seiten: 260 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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Deep Neural Evolution : Deep Learning With Evolutionary Computation
Book 32 of 32: Natural ComputingSeller: GreatBookPricesUK, Woodford Green, United Kingdom
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Evolutionary Approach to Machine Learning and Deep Neural Networks : Neuro-Evolution and Gene Regulatory Networks
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore, 2018
ISBN 10: 9811301999 ISBN 13: 9789811301995
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
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Evolutionary Approach to Machine Learning and Deep Neural Networks : Neuro-Evolution and Gene Regulatory Networks
Language: English
Published by Springer Nature Singapore, Springer Nature Singapore, 2019
ISBN 10: 9811343586 ISBN 13: 9789811343582
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields.Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution.The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
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Evolutionary Approach to Machine Learning and Deep Neural Networks: Neuro-evolution and Gene Regulatory Networks
Language: English
Published by Springer-Verlag New York Inc, 2018
ISBN 10: 9811301999 ISBN 13: 9789811301995
US$ 271.88
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Add to basketHardcover. Condition: Brand New. 260 pages. 9.25x6.10x0.79 inches. In Stock.
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Add to basketPaperback. Condition: New. New. book.
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