Mathematical Aspects Deep Learning (24 results)

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  • Language: English

    Published by Cambridge University Press, 2022

    1316516784 / 9781316516782

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  • Language: English

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  • Language: English

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  • Language: English

    Published by Cambridge University Press, 2023

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  • Language: English

    Published by Cambridge University Press, 2023

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  • Language: English

    Published by Cambridge University Press, 2023

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  • Language: English

    Published by Cambridge University Press, 2023

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  • Language: English

    Published by Cambridge University Press, 2022

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  • Language: English

    Published by Cambridge University Press 2022-12-22, 2022

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  • Language: English

    Published by Cambridge University Press, 2023

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  • Language: English

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  • Language: English

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  • Language: English

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  • Language: English

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    Hardback. Condition: New. In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.

  • Language: English

    Published by Cambridge University Press, 2022

    1316516784 / 9781316516782

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    Hardcover. Condition: Brand New. 485 pages. 10.00x7.00x1.00 inches. In Stock.

  • Language: English

    Published by Cambridge University Press, 2023

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  • Language: English

    Published by Cambridge University Press, 2023

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  • Language: English

    Published by Cambridge University Press, 2022

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    Condition: New. Inhaltsverzeichnis1. The modern mathematics of deep learning Julius Berner, Philipp Grohs, Gitta Kutyniok and Philipp Petersen 2. Generalization in deep learning Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio 3. Expressivity.

  • Language: English

    Published by Cambridge University Press, 2022

    1316516784 / 9781316516782

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    Hardcover. Condition: Brand New. 485 pages. 10.00x7.00x1.00 inches. In Stock.

  • Language: English

    Published by Cambridge University Press, GB, 2022

    1316516784 / 9781316516782

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    Hardback. Condition: New. In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.

  • Language: English

    Published by Cambridge University Press, 2023

    1316516784 / 9781316516782

    • Hardcover

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    Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.

  • Language: English

    Published by Cambridge University Press, 2023

    1316516784 / 9781316516782

    • Hardcover

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  • Language: English

    Published by Cambridge University Press, Cambridge, 2022

    1316516784 / 9781316516782

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    Hardcover. Condition: new. Hardcover. In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research. The development of a theoretical foundation for deep learning methods constitutes one of the most active and exciting research topics in applied mathematics. Written by leading experts in the field, this book acts as a mathematical introduction to deep learning for researchers and graduate students trying to get into the field. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

  • Language: English

    Published by Cambridge University Press, 2022

    1316516784 / 9781316516782

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    Buch. Condition: Neu. Mathematical Aspects of Deep Learning | Philipp Grohs (u. a.) | Buch | Gebunden | Englisch | 2022 | Cambridge University Press | EAN 9781316516782 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.