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Aggiungi al carrelloHRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Da: PBShop.store US, Wood Dale, IL, U.S.A.
HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 165,22
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Aggiungi al carrelloCondizione: New. In.
Da: Chiron Media, Wallingford, Regno Unito
EUR 163,91
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Aggiungi al carrelloHardcover. Condizione: New.
Condizione: As New. Unread book in perfect condition.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 182,61
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2017
ISBN 10: 3319663070 ISBN 13: 9783319663074
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 206,82
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Aggiungi al carrelloHardback. Condizione: New. 1st ed. 2018.
Da: Speedyhen, Hertfordshire, Regno Unito
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Aggiungi al carrelloCondizione: NEW.
Lingua: Inglese
Editore: Springer International Publishing, 2017
ISBN 10: 3319663070 ISBN 13: 9783319663074
Da: moluna, Greven, Germania
EUR 172,61
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Aggiungi al carrelloCondizione: New. Presents a unique overview of dedicated machine learning techniques for sensor dataFeatures hands-on exercises, including those related to mobile app developmentIllustrates the techniques by means of examples to make them more easily unders.
Condizione: New.
Lingua: Inglese
Editore: Springer, Springer International Publishing, 2017
ISBN 10: 3319663070 ISBN 13: 9783319663074
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 181,89
Quantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. Luckily, in the domain of artificial intelligence there are techniques that can help out: machine-learning approaches allow this type of data to be analyzed. While there are ample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users.
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2017
ISBN 10: 3319663070 ISBN 13: 9783319663074
Da: Rarewaves.com UK, London, Regno Unito
EUR 195,00
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Aggiungi al carrelloHardback. Condizione: New. 1st ed. 2018.
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 251,78
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Aggiungi al carrelloHardcover. Condizione: New. New. book.
Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2017
ISBN 10: 3319663070 ISBN 13: 9783319663074
Da: Revaluation Books, Exeter, Regno Unito
EUR 277,18
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Aggiungi al carrelloHardcover. Condizione: Brand New. 248 pages. 9.25x6.10x0.79 inches. In Stock.
Da: Brook Bookstore On Demand, Napoli, NA, Italia
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing Okt 2017, 2017
ISBN 10: 3319663070 ISBN 13: 9783319663074
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 181,89
Quantità: 2 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. Luckily, in the domain of artificial intelligence there are techniques that can help out: machine-learning approaches allow this type of data to be analyzed. While there are ample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users. 248 pp. Englisch.
Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2017
ISBN 10: 3319663070 ISBN 13: 9783319663074
Da: Revaluation Books, Exeter, Regno Unito
EUR 196,19
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Aggiungi al carrelloHardcover. Condizione: Brand New. 248 pages. 9.25x6.10x0.79 inches. In Stock. This item is printed on demand.
Da: preigu, Osnabrück, Germania
EUR 159,40
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Aggiungi al carrelloBuch. Condizione: Neu. Machine Learning for the Quantified Self | On the Art of Learning from Sensory Data | Mark Hoogendoorn (u. a.) | Buch | xv | Englisch | 2017 | Springer | EAN 9783319663074 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
Lingua: Inglese
Editore: Springer, Springer International Publishing Okt 2017, 2017
ISBN 10: 3319663070 ISBN 13: 9783319663074
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 181,89
Quantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. Luckily, in the domain of artificial intelligence there are techniques that can help out: machine-learning approaches allow this type of data to be analyzed. While there are ample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 248 pp. Englisch.
Da: Majestic Books, Hounslow, Regno Unito
EUR 250,40
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. Print on Demand.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 251,15
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.