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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Dieses Buch ist so aufgebaut, dass Ihnen die Inhalte im Rahmen der geschichtlichen Entwicklung der KI vermittelt werden. Aus diesem Grund beginnt unsere Reise mit dem Punkt 'Einführung und geschichtlicher Hintergrund der KI'.Im zweiten Abschnitt greifen wir zunächst die anfänglichen Techniken der KI auf. Sie lernen dabei die Konzepte und berühmte Beispielsysteme kennen, die diese frühe Phase der Euphorie auslösten.Im dritten Abschnitt behandeln wir Expertensysteme, die ähnlich zu den allgemeinen Problemlösern nur spezielle Probleme behandeln. Aber dafür exzessiv auf Regeln und Fakten in Form einer Wissensbasis zugreifen.Der vierte Abschnitt läutet die Rückkehr zu der Idee ein, das menschliche Gehirn nachbauen zu können und so in Form von neuronalen Netzen der digitalen Informationsverarbeitung zugänglich zu machen. Wir betrachten die frühen Ansätze und stellen heraus, welche Ideen noch gefehlt haben, um neuronalen Netzen zum Durchbruch zu verhelfen.Die Idee eines Agenten und ihr Zusammenspiel in einem Multi-Agenten-System wird im fünften Abschnitt beschrieben. Ein solches System dient im Wesentlichen dazu, Komplexität auf mehrere Instanzen zu verteilen.Der sechste Abschnitt behandelt den Durchbruch der mehrschichtigen neuronalen Netze, maschinelles Lernen, maschinelle Sehen (Computer Vision), Spracherkennung und viele weitere Anwendungen der heutigen KI.Für alle Lerneinheiten ist es unser Ziel eine Grundlage zu schaffen, um verschiedene Begriffe wie 'Computer Vision' und 'Machine-Learning' einordnen und bewerten zu können.Nach diesem Buch sind Sie in der Lage, sich vertieft mit den Themengebieten Ihrer Wahl zu befassen.
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Add to basketXVII, 299 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. The Springer Series on Challenges in Machine Learning. Sprache: Englisch.
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Add to basketxiv, 208 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.
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Published by Institute of Physics Publishing, 2024
ISBN 10: 0750362456 ISBN 13: 9780750362450
Language: English
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Add to basketCondition: Gut. Zustand: Gut | Seiten: 364 | Sprache: Englisch | Produktart: Bücher.
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Add to basketCondition: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.
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Published by Packt Publishing 2019-02-21, 2019
ISBN 10: 1789613965 ISBN 13: 9781789613964
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Published by Orange Education Pvt Ltd, 2023
ISBN 10: 9388590872 ISBN 13: 9789388590877
Language: English
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Published by Pastor Publishing Ltd, 2024
ISBN 10: 1839386266 ISBN 13: 9781839386268
Language: English
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Published by Packt Publishing 2018-03, 2018
ISBN 10: 1788398068 ISBN 13: 9781788398060
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Published by Independently published, 2018
ISBN 10: 1976961335 ISBN 13: 9781976961335
Language: German
Seller: Revaluation Books, Exeter, United Kingdom
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Seller: Austin Goodwill 1101, Austin, TX, U.S.A.
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Add to basketCondition: Good. Get fast and secure shipping knowing your purchase helps empower our community to transform their lives through work.
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ISBN 10: 1484275225 ISBN 13: 9781484275221
Language: English
Seller: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
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Seller: Chiron Media, Wallingford, United Kingdom
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Published by O'Reilly Media (edition 1), 2021
ISBN 10: 1098102363 ISBN 13: 9781098102364
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Published by Packt Publishing 2020-05, 2020
ISBN 10: 1838827064 ISBN 13: 9781838827069
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Published by Springer Berlin Heidelberg, 2012
ISBN 10: 3642286607 ISBN 13: 9783642286605
Language: English
Seller: Buchpark, Trebbin, Germany
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Add to basketCondition: Hervorragend. Zustand: Hervorragend | Seiten: 272 | Sprache: Englisch | Produktart: Bücher.
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Seller: Bookmonger.Ltd, HILLSIDE, NJ, U.S.A.
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Add to basketpaperback. Condition: Very Good. Crease on cover and a few pages*.
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Published by Independently published 2019-01, 2019
ISBN 10: 1796607266 ISBN 13: 9781796607260
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Published by Packt Publishing, 2018
Language: English
Seller: Books in my Basket, New Delhi, India
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Add to basketSoft cover. Condition: New. ISBN:9781788398060,Territorial restriction maybe printed on the book. This is an Int'l edition, ISBN and cover may differ from US edition, Contents same as US edition.
Published by Springer International Publishing, 2007
ISBN 10: 3031011147 ISBN 13: 9783031011146
Language: English
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This lecture presents research on a general framework for perceptual organization that was conducted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organization methodology applicable in situations that may seem heterogeneous initially. We show how several problems can be posed as the organization of the inputs into salient perceptual structures, which are inferred via tensor voting. The work presented here extends the original tensor voting framework with the addition of boundary inference capabilities; a novel re-formulation of the framework applicable to high-dimensional spaces and the development of algorithms for computer vision and machine learning problems. We show complete analysis for some problems, while we briefly outline our approach for other applications and provide pointers to relevant sources.