Sprache: Englisch
Verlag: Cambridge University Press, 2022
ISBN 10: 1316519333 ISBN 13: 9781316519332
Anbieter: Books From California, Simi Valley, CA, USA
hardcover. Zustand: Acceptable. Damage to spine. Cover and edges may have some wear. Good reading copy.
Sprache: Englisch
Verlag: Cambridge University Press (edition New), 2022
ISBN 10: 1316519333 ISBN 13: 9781316519332
Anbieter: BooksRun, Philadelphia, PA, USA
Hardcover. Zustand: Very Good. New. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
Sprache: Englisch
Verlag: Cambridge University Press, 2022
ISBN 10: 1316519333 ISBN 13: 9781316519332
Anbieter: Books From California, Simi Valley, CA, USA
hardcover. Zustand: Very Good.
Sprache: Englisch
Verlag: Cambridge University Press, 2022
ISBN 10: 1316519333 ISBN 13: 9781316519332
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 81,08
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Sprache: Englisch
Verlag: Cambridge University Press, 2022
ISBN 10: 1316519333 ISBN 13: 9781316519332
Anbieter: Kennys Bookstore, Olney, MD, USA
Zustand: New. 2022. New. Hardcover. . . . . . Books ship from the US and Ireland.
Sprache: Englisch
Verlag: Cambridge University Press, 2022
ISBN 10: 1316519333 ISBN 13: 9781316519332
Anbieter: moluna, Greven, Deutschland
Gebunden. Zustand: New. This is the first book focused entirely on deep learning theory. Tools from theoretical physics are borrowed and adapted to explain, from first principles, how realistic deep neural networks work, benefiting practitioners looking to build better AI models a.
Sprache: Englisch
Verlag: Cambridge University Press, 2022
ISBN 10: 1316519333 ISBN 13: 9781316519332
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 124,32
Anzahl: 2 verfügbar
In den WarenkorbHardcover. Zustand: Brand New. 390 pages. 10.00x7.00x1.00 inches. In Stock.
Sprache: Englisch
Verlag: Cambridge University Press, 2022
ISBN 10: 1316519333 ISBN 13: 9781316519332
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first-principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self-contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus, and informal probability theory, and it can easily fill a semester-long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be matched with a set of effective principles, providing a timeless blueprint for theoretical research in deep learning.