Publicado por Pocket Books, New York, 1994
Librería: WF Sandercombe, Burlington, ON, Canada
Original o primera edición
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Añadir al carritoSoft cover. Condición: Very Good. 1st Edition. (xxxiii) 280 pp. Light edge and corner wear with a faint crease on the spine; no interior markings. Cover art by Gerber Studio. This anthology contains: Of Sex andHorror by Forrest J. Ackerman; Introduction; Things of Which We Do Not Speak by Lucy Taylor; Mr. Torso by Edward Lee; Give It to Me, Baby by Sidney Williams; Immaterial Girl by Michael Garrett; A Moment of Ecstasy by Graham Watkins; Reincarnal by Max Allan Collins; Yet Another Poisoned Apple for the Fairy Princess by A. R. Morlan; Sex Starved by Edo van Belkom; TheRose by Jack Ketchum; Release of Flesh by Steve Rasnic Tem; The Numbers Game by Bentley Little; Heretical Visions by Claudia O'Keefe; and Suffer Kate by Graham Masterton; followed by The Contributors. Book.
Publicado por George Newnes Ltd,, 1945
Librería: Shore Books, London, Reino Unido
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Añadir al carritoSoft cover. Condición: Very Good. 72 pages. Leslie Bussey "Funny, Isn't It?" / Mabel Stonier "Tess Of The Timber Corps" / Jego "Rattlin's Haircut" / Graham White "Clever Stuff" / Trevor Allen "How To Keep A Cook" / Max Dunstone "A Mug's Game" / George Maracco "Little Mother" (SL#48).
Idioma: Inglés
Publicado por Oxford University Press, 2024
ISBN 10: 0198896557 ISBN 13: 9780198896555
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Publicado por Oxford University Press, 2024
ISBN 10: 0198896557 ISBN 13: 9780198896555
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ISBN 10: 0198896557 ISBN 13: 9780198896555
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ISBN 10: 0198896557 ISBN 13: 9780198896555
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ISBN 10: 0198896557 ISBN 13: 9780198896555
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ISBN 10: 0198896557 ISBN 13: 9780198896555
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Publicado por Oxford University Press, Oxford, 2024
ISBN 10: 0198896557 ISBN 13: 9780198896555
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Añadir al carritoPaperback. Condición: new. Paperback. This book describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Taking a gradual approach, it builds up concepts in a solid, step-by-step fashion so that the ideas and algorithms can be implemented in practical software applications.Digital signal processing (DSP) isone of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relativenewcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance, and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference.DSPand statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of keytopics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered, yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts andalgorithms in this important topic. Describes the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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ISBN 10: 0198896557 ISBN 13: 9780198896555
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Publicado por Oxford University Press, 2024
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Publicado por Oxford University Press, USA, 2024
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Publicado por Oxford University Press, 2024
ISBN 10: 0198896557 ISBN 13: 9780198896555
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Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.
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Publicado por OXFORD UNIVERSITY PRESS, 2024
ISBN 10: 0198896557 ISBN 13: 9780198896555
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Añadir al carritoPaperback. Condición: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Publicado por Oxford University Press, 2024
ISBN 10: 0198896557 ISBN 13: 9780198896555
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Añadir al carritoPaperback. Condición: Brand New. 384 pages. 9.65x7.48x0.79 inches. In Stock.
Idioma: Inglés
Publicado por Oxford University Press, Oxford, 2024
ISBN 10: 0198896557 ISBN 13: 9780198896555
Librería: CitiRetail, Stevenage, Reino Unido
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Añadir al carritoPaperback. Condición: new. Paperback. This book describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Taking a gradual approach, it builds up concepts in a solid, step-by-step fashion so that the ideas and algorithms can be implemented in practical software applications.Digital signal processing (DSP) isone of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relativenewcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance, and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference.DSPand statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of keytopics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered, yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts andalgorithms in this important topic. Describes the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Idioma: Inglés
Publicado por Oxford University Press Mai 2024, 2024
ISBN 10: 0198896557 ISBN 13: 9780198896555
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 57,08
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - This book describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Taking a gradual approach, it builds up concepts in a solid, step-by-step fashion so that the ideas and algorithms can be implemented in practical software applications.Digital signal processing (DSP) is one of the 'foundational' engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance, and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference.DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered, yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in this important topic.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 114,01
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Añadir al carritoHardcover. Condición: Brand New. 359 pages. 10.00x7.75x1.00 inches. In Stock.