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paperback. Zustand: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!
Anbieter: GreatBookPrices, Columbia, MD, USA
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Anbieter: GreatBookPrices, Columbia, MD, USA
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Anbieter: BargainBookStores, Grand Rapids, MI, USA
Paperback or Softback. Zustand: New. Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning. Book.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 32,78
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Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
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Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
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Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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Anbieter: ChouetteCoop, Kervignac, Frankreich
Zustand: Used: Good. Occasion - Bon Etat - Artificial intelligence engines : A tutorial introduction to the mathematics of deep learning (2019) - Grand Format.
Anbieter: PBShop.store US, Wood Dale, IL, USA
PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The brain has always had a fundamental advantage over conventional computers: it can learn. However, a new generation of artificial intelligence algorithms, in the form of deep neural networks, is rapidly eliminating that advantage. Deep neural networks rely on adaptive algorithms to master a wide variety of tasks, including cancer diagnosis, object recognition, speech recognition, robotic control, chess, poker, backgammon and Go, at super-human levels of performance.In this richly illustrated book, key neural network learning algorithms are explained informally first, followed by detailed mathematical analyses. Topics include both historically important neural networks (e.g. perceptrons), and modern deep neural networks (e.g. generative adversarial networks). Online computer programs, collated from open source repositories, give hands-on experience of neural networks, and PowerPoint slides provide support for teaching. Written in an informal style, with a comprehensive glossary, tutorial appendices (e.g. Bayes' theorem), and a list of further readings, this is an ideal introduction to the algorithmic engines of modern artificial intelligence.
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Artificial Intelligence Engines | A Tutorial Introduction to the Mathematics of Deep Learning | James V Stone | Taschenbuch | Kartoniert / Broschiert | Englisch | 2019 | Sebtel Press | EAN 9780956372819 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 111,88
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Anbieter: Books Puddle, New York, NY, USA
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Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Zustand: New. PRINT ON DEMAND.
Anbieter: preigu, Osnabrück, Deutschland
Buch. Zustand: Neu. Artificial Intelligence Engines | A Tutorial Introduction to the Mathematics of Deep Learning | James V Stone | Buch | Gebunden | Englisch | 2019 | Sebtel Press | EAN 9780956372826 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The brain has always had a fundamental advantage over conventional computers: it can learn. However, a new generation of artificial intelligence algorithms, in the form of deep neural networks, is rapidly eliminating that advantage. Deep neural networks rely on adaptive algorithms to master a wide variety of tasks, including cancer diagnosis, object recognition, speech recognition, robotic control, chess, poker, backgammon and Go, at super-human levels of performance.In this richly illustrated book, key neural network learning algorithms are explained informally first, followed by detailed mathematical analyses. Topics include both historically important neural networks (e.g. perceptrons), and modern deep neural networks (e.g. generative adversarial networks). Online computer programs, collated from open source repositories, give hands-on experience of neural networks, and PowerPoint slides provide support for teaching. Written in an informal style, with a comprehensive glossary, tutorial appendices (e.g. Bayes' theorem), and a list of further readings, this is an ideal introduction to the algorithmic engines of modern artificial intelligence.