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Añadir al carritoCondición: 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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Publicado por Springer
Librería: Academic Book Solutions, Medford, NY, Estados Unidos de America
EUR 9,18
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Añadir al carritohardcover. Condición: VeryGood. A copy that may have been read, very minimal wear and tear. May have a remainder mark.
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Librería: Academic US, Piscataway, NJ, Estados Unidos de America
EUR 35,05
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Añadir al carritopaperback. Condición: 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!
EUR 35,89
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Publicado por Springer Nature Switzerland AG, 2022
ISBN 10: 3030931579 ISBN 13: 9783030931575
Idioma: Inglés
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
EUR 35,31
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Añadir al carritoHRD. Condición: Used - Good. Used Book. Shipped from UK. Established seller since 2000.
Librería: Books From California, Simi Valley, CA, Estados Unidos de America
EUR 38,52
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EUR 47,38
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Librería: Academic US, Piscataway, NJ, Estados Unidos de America
EUR 46,18
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EUR 46,98
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EUR 47,34
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
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Añadir al carritoCondición: New.
EUR 47,84
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Añadir al carritoCondición: New.
Publicado por Springer Nature Switzerland AG, Cham, 2023
ISBN 10: 3030931609 ISBN 13: 9783030931605
Idioma: Inglés
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 60,95
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Añadir al carritoPaperback. Condición: 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.
Publicado por Springer Nature Switzerland AG, CH, 2023
ISBN 10: 3030931609 ISBN 13: 9783030931605
Idioma: Inglés
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 61,86
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: 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.
EUR 51,00
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Añadir al carritoCondición: New. In.
EUR 47,33
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Añadir al carritoCondición: New.
Publicado por Springer Nature Switzerland AG, 2022
ISBN 10: 3030931579 ISBN 13: 9783030931575
Idioma: Inglés
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
EUR 65,40
Cantidad disponible: 1 disponibles
Añadir al carritoHRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Publicado por Springer-Nature New York Inc, 2023
ISBN 10: 3030931609 ISBN 13: 9783030931605
Idioma: Inglés
Librería: Revaluation Books, Exeter, Reino Unido
EUR 58,13
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Añadir al carritoPaperback. Condición: Brand New. 215 pages. 9.25x6.10x0.46 inches. In Stock.
Publicado por Springer Nature Switzerland AG, 2022
ISBN 10: 3030931579 ISBN 13: 9783030931575
Idioma: Inglés
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
EUR 65,16
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Añadir al carritoHRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Publicado por Springer Nature Switzerland AG, 2023
ISBN 10: 3030931609 ISBN 13: 9783030931605
Idioma: Inglés
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Original o primera edición
EUR 58,41
Cantidad disponible: 2 disponibles
Añadir al carritoCondición: New. 2023. 1st ed. 2022. Paperback. . . . . .
EUR 53,77
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Añadir al carritoCondición: As New. Unread book in perfect condition.
ISBN 10: 3031640861 ISBN 13: 9783031640865
Idioma: Inglés
Librería: Books From California, Simi Valley, CA, Estados Unidos de America
EUR 49,89
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Añadir al carritohardcover. Condición: Very Good.
Publicado por Springer Nature Switzerland AG, 2023
ISBN 10: 3030931609 ISBN 13: 9783030931605
Idioma: Inglés
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
EUR 71,68
Cantidad disponible: 2 disponibles
Añadir al carritoCondición: New. 2023. 1st ed. 2022. Paperback. . . . . . Books ship from the US and Ireland.