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Ajouter au panierEtat : good. The item shows wear from consistent use, but it remains in good condition and works perfectly. All pages and cover are intact including the dust cover, if applicable . Spine may show signs of wear. Pages may include limited notes and highlighting. May NOT include discs, access code or other supplemental materials.
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Edité par Springer
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Ajouter au panierhardcover. Etat : VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.
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Ajouter au panierpaperback. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Edité par Springer Nature Switzerland AG, 2022
ISBN 10 : 3030931579 ISBN 13 : 9783030931575
Langue: anglais
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Ajouter au panierEtat : New.
Edité par Springer Nature Switzerland AG, Cham, 2023
ISBN 10 : 3030931609 ISBN 13 : 9783030931605
Langue: anglais
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EUR 60,95
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Ajouter au panierPaperback. Etat : new. Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Edité par Springer Nature Switzerland AG, CH, 2023
ISBN 10 : 3030931609 ISBN 13 : 9783030931605
Langue: anglais
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni
EUR 62,15
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Ajouter au panierPaperback. Etat : 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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Ajouter au panierEtat : New. In.
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Ajouter au panierEtat : New.
Edité par Springer Nature Switzerland AG, 2022
ISBN 10 : 3030931579 ISBN 13 : 9783030931575
Langue: anglais
Vendeur : PBShop.store US, Wood Dale, IL, Etats-Unis
EUR 65,40
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Ajouter au panierHRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.
Edité par Springer-Nature New York Inc, 2023
ISBN 10 : 3030931609 ISBN 13 : 9783030931605
Langue: anglais
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 58,13
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Ajouter au panierPaperback. Etat : Brand New. 215 pages. 9.25x6.10x0.46 inches. In Stock.
Edité par Springer Nature Switzerland AG, 2022
ISBN 10 : 3030931579 ISBN 13 : 9783030931575
Langue: anglais
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
EUR 65,16
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Ajouter au panierHRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.
Edité par Springer Nature Switzerland AG, 2023
ISBN 10 : 3030931609 ISBN 13 : 9783030931605
Langue: anglais
Vendeur : Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlande
Edition originale
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Ajouter au panierEtat : New. 2023. 1st ed. 2022. Paperback. . . . . .
EUR 53,77
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Ajouter au panierEtat : As New. Unread book in perfect condition.
ISBN 10 : 3031640861 ISBN 13 : 9783031640865
Langue: anglais
Vendeur : Books From California, Simi Valley, CA, Etats-Unis
EUR 49,89
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Ajouter au panierhardcover. Etat : Very Good.
Edité par Springer Nature Switzerland AG, 2023
ISBN 10 : 3030931609 ISBN 13 : 9783030931605
Langue: anglais
Vendeur : Kennys Bookstore, Olney, MD, Etats-Unis
EUR 71,68
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Ajouter au panierEtat : New. 2023. 1st ed. 2022. Paperback. . . . . . Books ship from the US and Ireland.