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In den WarenkorbHardcover. Zustand: new. New Copy. Customer Service Guaranteed.
Verlag: Springer Nature Switzerland AG, 2022
ISBN 10: 3030931579 ISBN 13: 9783030931575
Sprache: Englisch
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ISBN 10: 3030931579 ISBN 13: 9783030931575
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In den WarenkorbHardcover. Zustand: Very good. Hardcover Octavo. illustrated boards, 197 pp Standard shipping (no tracking or insurance) / Priority (with tracking) / Custom quote for large or heavy orders.
ISBN 10: 3031640861 ISBN 13: 9783031640865
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ISBN 10: 3030931579 ISBN 13: 9783030931575
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Verlag: Springer International Publishing, Springer International Publishing Sep 2024, 2024
ISBN 10: 3031640861 ISBN 13: 9783031640865
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In den WarenkorbBuch. Zustand: Neu. Neuware 340 pp. Englisch.
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ISBN 10: 3030931579 ISBN 13: 9783030931575
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In den WarenkorbHardback. Zustand: New. 2022 ed. 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.
ISBN 10: 3031640861 ISBN 13: 9783031640865
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In den WarenkorbZustand: New. This first comprehensive book on models behind Generative AI has been thoroughly revised to cover all major classes of deep generative models: mixture models, Probabilistic Circuits, Autoregressive Models, Flow-based Models, Latent Variable Models, GANs,.
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In den Warenkorbhardcover. Zustand: VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.
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ISBN 10: 3031640861 ISBN 13: 9783031640865
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In den WarenkorbBuch. Zustand: Neu. Neuware Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 340 pp. Englisch.
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In den WarenkorbHardcover. Zustand: Brand New. 215 pages. 9.25x6.10x0.59 inches. In Stock.
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ISBN 10: 3031640861 ISBN 13: 9783031640865
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ISBN 10: 3031640861 ISBN 13: 9783031640865
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In den WarenkorbBuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering.
Verlag: Springer, Berlin, Springer International Publishing, Springer, 2022
ISBN 10: 3030931579 ISBN 13: 9783030931575
Sprache: Englisch
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In den WarenkorbBuch. Zustand: Neu. 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.
Verlag: Springer Nature Switzerland AG, CH, 2022
ISBN 10: 3030931579 ISBN 13: 9783030931575
Sprache: Englisch
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In den WarenkorbHardback. Zustand: New. 2022 ed. 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.
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In den Warenkorbhardcover. Zustand: New. New. book.
ISBN 10: 3031640861 ISBN 13: 9783031640865
Sprache: Englisch
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