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Artificial Intelligence Engines Tutorial by Stone James (23 results)
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Paperback. Condition: 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!
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Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
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Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
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Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning (Paperback or Softback)
Published by Sebtel Press 4/1/2019, 2019
ISBN 10: 0956372813 ISBN 13: 9780956372819
Language: English
Paperback or Softback. Condition: New. Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning. Book.
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Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
Published by Sebtel Press 2019-04, 2019
ISBN 10: 0956372813 ISBN 13: 9780956372819
Language: English
PF. Condition: New.
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Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
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Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
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Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
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Condition: Used: Good. Occasion - Bon Etat - Artificial intelligence engines : A tutorial introduction to the mathematics of deep learning (2019) - Grand Format.
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Artificial Intelligence Engines A Tutorial Introduction to the Mathematics of Deep Learning
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Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
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Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
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Add to basketHardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 434.
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Taschenbuch. Condition: 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.
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Taschenbuch. Condition: 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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Buch. Condition: 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.
