Langue: anglais
Edité par Sebtel Press (edition ), 2019
ISBN 10 : 0956372813 ISBN 13 : 9780956372819
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Ajouter au panierpaperback. Etat : 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!
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Ajouter au panierPaperback. Etat : Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
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Ajouter au panierPaperback or Softback. Etat : New. Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning. Book.
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Ajouter au panierEtat : Used: Good. Occasion - Bon Etat - Artificial intelligence engines : A tutorial introduction to the mathematics of deep learning (2019) - Grand Format.
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Ajouter au panierPAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Ajouter au panierPaperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Ajouter au panierHardback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Ajouter au panierTaschenbuch. Etat : 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.
Vendeur : preigu, Osnabrück, Allemagne
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Ajouter au panierTaschenbuch. Etat : 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.
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Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Ajouter au panierEtat : New. PRINT ON DEMAND.
Vendeur : preigu, Osnabrück, Allemagne
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Ajouter au panierBuch. Etat : 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.
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 103,16
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Ajouter au panierBuch. Etat : 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.