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Editore: LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838363477ISBN 13: 9783838363479
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
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Editore: Springer, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
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Editore: Springer, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
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Editore: Springer, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
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Editore: Springer, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
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Editore: Springer, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
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Condizione: As New. Unread book in perfect condition.
Editore: Springer International Publishing 2023-02-20, Berlin, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
Da: Blackwell's, London, Regno Unito
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paperback. Condizione: New. Language: ENG.
Editore: Springer Nature Switzerland AG, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
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Editore: Springer 2/20/2023, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
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Paperback or Softback. Condizione: New. Deep Generative Modeling 0.68. Book.
Editore: LAP Lambert Academic Publishing, 2010
ISBN 10: 3838363477ISBN 13: 9783838363479
Da: Ria Christie Collections, Uxbridge, Regno Unito
Libro Print on Demand
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Editore: LAP Lambert Academic Publishing 2010-05, 2010
ISBN 10: 3838363477ISBN 13: 9783838363479
Da: Chiron Media, Wallingford, Regno Unito
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PF. Condizione: New.
Editore: Springer, 2022
ISBN 10: 3030931579ISBN 13: 9783030931575
Da: GreatBookPrices, Columbia, MD, U.S.A.
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Condizione: New.
Editore: Springer, 2022
ISBN 10: 3030931579ISBN 13: 9783030931575
Da: booksXpress, Bayonne, NJ, U.S.A.
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Hardcover. Condizione: new.
Editore: Springer Nature Switzerland AG, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
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Condizione: New. 2023. 1st ed. 2022. Paperback. . . . . .
Editore: Springer, 2022
ISBN 10: 3030931579ISBN 13: 9783030931575
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Condizione: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.
Editore: Springer, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
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Editore: Springer, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
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Editore: Springer, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
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Paperback. Condizione: very good. In Used Condition.
Editore: LAP LAMBERT Academic Publishing Mai 2010, 2010
ISBN 10: 3838363477ISBN 13: 9783838363479
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Libro Print on Demand
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data mining and knowledge extraction methods become ones of the most important issues in modern computer science. Moreover, those methods have many real-life applications, e.g. in economics, medicine, computer networks, etc. Therefore, there is a constant need for developing new knowledge representations and knowledge extraction methods. In this work a coherent survey of problems connected with relational knowledge representation and methods for achieving relational knowledge representation were presented. Proposed approach was shown on three applications: economic case, biomedical case and benchmark dataset. All crucial definitions were formulated and three main methods for relation identification problem were shown. Moreover, for specific relational models and observations types different identification methods were presented. Furthermore, if problem formulation includes uncertainty characteristics, a general approach with soft variables was proposed. 100 pp. Englisch.
Editore: LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838363477ISBN 13: 9783838363479
Da: PBShop.store US, Wood Dale, IL, U.S.A.
Libro Print on Demand
PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Editore: Springer, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
Da: GreatBookPricesUK, Castle Donington, DERBY, Regno Unito
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Condizione: As New. Unread book in perfect condition.
Editore: Springer International Publishing Feb 2023, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Libro Print on Demand
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective 'deep' comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions.Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github.The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them. 216 pp. Englisch.
Editore: Springer, 2022
ISBN 10: 3030931579ISBN 13: 9783030931575
Da: GreatBookPrices, Columbia, MD, U.S.A.
Libro
Condizione: As New. Unread book in perfect condition.
Editore: Springer-Nature New York Inc, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
Da: Revaluation Books, Exeter, Regno Unito
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Paperback. Condizione: Brand New. 215 pages. 9.25x6.10x0.46 inches. In Stock.
Editore: LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838363477ISBN 13: 9783838363479
Da: AHA-BUCH GmbH, Einbeck, Germania
Libro Print on Demand
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data mining and knowledge extraction methods become ones of the most important issues in modern computer science. Moreover, those methods have many real-life applications, e.g. in economics, medicine, computer networks, etc. Therefore, there is a constant need for developing new knowledge representations and knowledge extraction methods. In this work a coherent survey of problems connected with relational knowledge representation and methods for achieving relational knowledge representation were presented. Proposed approach was shown on three applications: economic case, biomedical case and benchmark dataset. All crucial definitions were formulated and three main methods for relation identification problem were shown. Moreover, for specific relational models and observations types different identification methods were presented. Furthermore, if problem formulation includes uncertainty characteristics, a general approach with soft variables was proposed.
Editore: LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838363477ISBN 13: 9783838363479
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
Libro Print on Demand
PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Editore: Springer Nature Switzerland AG, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Editore: Springer, 2022
ISBN 10: 3030931579ISBN 13: 9783030931575
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
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Condizione: New.
Editore: Springer Nature Switzerland AG, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
Da: Kennys Bookstore, Olney, MD, U.S.A.
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Condizione: New. 2023. 1st ed. 2022. Paperback. . . . . . Books ship from the US and Ireland.
Editore: Springer International Publishing, 2023
ISBN 10: 3030931609ISBN 13: 9783030931605
Da: AHA-BUCH GmbH, Einbeck, Germania
Libro
Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective 'deep' comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions.Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github.The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.