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Unsupervised Learning Foundations Neural (13 results)
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US$ 61.27
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Unsupervised Learning: Foundations of Neural Computation (Paperback or Softback)
Published by MIT Press (MA) 5/24/1999, 1999
ISBN 10: 026258168X ISBN 13: 9780262581684
Language: English
Paperback or Softback. Condition: New. Unsupervised Learning: Foundations of Neural Computation 1.32. Book.
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US$ 60.59
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US$ 70.90
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Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 69.19
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US$ 75.00
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Unsupervised Learning : Foundations of Neural Computation
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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US$ 69.17
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Unsupervised Learning : Foundations of Neural Computation
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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US$ 75.84
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US$ 94.69
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Add to basketPaperback. Condition: Brand New. 350 pages. 8.75x5.75x0.75 inches. In Stock.
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paperback. Condition: New. In shrink wrap. Looks like an interesting title!
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Unsupervised Learning: Foundations of Neural Computation [Paperback] Hinton, Geoffrey and Sejnowski, Terrence J.
Seller: Bookmanns UK Based, Family Run Business., Wigan, United Kingdom
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US$ 41.43
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Add to basketCondition: g. Unsupervised Learning: Foundations of Neural Computation [Paperback] Hinton, Geoffrey and Sejnowski, Terrence J.
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Unsupervised Learning: Foundations of Neural Computation
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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US$ 76.37
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 574.
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US$ 77.48
Convert currencyUS$ 35.71 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketTaschenbuch. Condition: 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.
