Da: HPB-Diamond, Dallas, TX, U.S.A.
paperback. Condizione: 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!
Lingua: Inglese
Editore: MIT Press, Cambridge, Massachussetts, 1999
ISBN 10: 026258168X ISBN 13: 9780262581684
Da: Ethan Daniel Books, Toronto, ON, Canada
Prima edizione
EUR 31,04
Quantità: 1 disponibili
Aggiungi al carrelloLight spotting to fore-edge of text block, but not to inside pages. Otherwise a clean copy with no ownership marks or underlining and no reading crease to spine. 398 pages. s254.
Da: Booty, Bristol, Regno Unito
EUR 17,12
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EUR 54,93
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 53,74
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EUR 62,35
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 58,69
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 58,68
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 65,08
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Da: BargainBookStores, Grand Rapids, MI, U.S.A.
Paperback or Softback. Condizione: New. Unsupervised Learning: Foundations of Neural Computation. Book.
Da: Revaluation Books, Exeter, Regno Unito
EUR 81,19
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 350 pages. 8.75x5.75x0.75 inches. In Stock.
Da: BennettBooksLtd, Los Angeles, CA, U.S.A.
paperback. Condizione: New. In shrink wrap. Looks like an interesting title!
Da: Kennys Bookstore, Olney, MD, U.S.A.
EUR 115,35
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Aggiungi al carrelloCondizione: New. This volume on neural network learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. Editor(s): Hinton, Geoffrey E.; Sejnowski, Terrence J. Series: Computational Neuroscience. Num Pages: 414 pages, Illustrations. BIC Classification: PSAN; UGK; UYA; UYQN. Category: (P) Professional & Vocational. Dimension: 229 x 152 x 22. Weight in Grams: 544. . 1999. Illustrated. paperback. . . . . Books ship from the US and Ireland.
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 130,85
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. This volume on neural network learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. Editor(s): Hinton, Geoffrey E.; Sejnowski, Terrence J. Series: Computational Neuroscience. Num Pages: 414 pages, Illustrations. BIC Classification: PSAN; UGK; UYA; UYQN. Category: (P) Professional & Vocational. Dimension: 229 x 152 x 22. Weight in Grams: 544. . 1999. Illustrated. paperback. . . . .
Da: Majestic Books, Hounslow, Regno Unito
EUR 58,16
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. Print on Demand pp. 414.
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. Print on Demand pp. 414 First Edition.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 58,71
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 414.
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 67,06
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Aggiungi al carrelloPaperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Da: preigu, Osnabrück, Germania
EUR 50,35
Quantità: 5 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Unsupervised Learning | Foundations of Neural Computation | Geoffrey Hinton (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 1999 | MIT Press | EAN 9780262581684 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 60,14
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This volume, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.