Da: medimops, Berlin, Germania
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Da: Majestic Books, Hounslow, Regno Unito
EUR 45,29
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Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 45,90
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 56,58
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Da: Basi6 International, Irving, TX, U.S.A.
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Da: California Books, Miami, FL, U.S.A.
EUR 66,15
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 63,83
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 56,10
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 60,55
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Da: Chiron Media, Wallingford, Regno Unito
EUR 56,92
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 67,22
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Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2018
ISBN 10: 3319730037 ISBN 13: 9783319730035
Da: Revaluation Books, Exeter, Regno Unito
EUR 83,88
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 191 pages. 9.25x6.00x0.75 inches. In Stock.
Da: Mooney's bookstore, Den Helder, Paesi Bassi
EUR 83,67
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Aggiungi al carrelloCondizione: Very good.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 58,84
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.
Da: preigu, Osnabrück, Germania
EUR 54,80
Quantità: 5 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Introduction to Deep Learning | From Logical Calculus to Artificial Intelligence | Sandro Skansi | Taschenbuch | Undergraduate Topics in Computer Science | xiii | Englisch | 2018 | Springer | EAN 9783319730035 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 50,23
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer, Springer Feb 2018, 2018
ISBN 10: 3319730037 ISBN 13: 9783319730035
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 58,84
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology. 208 pp. Englisch.
Lingua: Inglese
Editore: Springer International Publishing, 2018
ISBN 10: 3319730037 ISBN 13: 9783319730035
Da: moluna, Greven, Germania
EUR 51,51
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers a welcome clarity of expression, maintaining mathematical rigor yet presenting the ideas in an intuitive and colourful manner Includes references to open problems studied in other disciplines, enabling the reader to pursue these topics on .
Lingua: Inglese
Editore: Springer, Springer Feb 2018, 2018
ISBN 10: 3319730037 ISBN 13: 9783319730035
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 58,84
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 208 pp. Englisch.