Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data - Couverture souple

Livre 22 sur 31: Cognitive Systems Monographs

Hoogendoorn, Mark ; Funk, Burkhardt

9783319882154: Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data

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ISBN 10 : 3319882155 ISBN 13 : 9783319882154

Editeur : Springer International Publishing AG, 2018

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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.

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�diteur: Springer International Publishing AG
Date d'�dition: 2018
Langue: anglais
ISBN 10: 3319882155
ISBN 13: 9783319882154
Reliure: Broch�
Nombre de pages: 246
Coordonn�es du fabricant: ProductSafety@springernature.com
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Hoogendoorn, Mark

Edit� par Springer, 2018

ISBN 10 : 3319882155 ISBN 13 : 9783319882154

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Machine Learning for the Quantified Self

Mark Hoogendoorn|Burkhardt Funk

Edit� par Springer International Publishing, 2018

ISBN 10 : 3319882155 ISBN 13 : 9783319882154

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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. N� de r�f. du vendeur 448761342

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Machine Learning for the Quantified Self

Burkhardt Funk

Edit� par Springer International Publishing Aug 2018, 2018

ISBN 10 : 3319882155 ISBN 13 : 9783319882154

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783319882154

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Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data: 35 (Cognitive Systems Monographs)

Hoogendoorn, Mark; Funk, Burkhardt

Edit� par Springer, 2018

ISBN 10 : 3319882155 ISBN 13 : 9783319882154

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Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data: 35 (Cognitive Systems Monographs)

Hoogendoorn, Mark; Funk, Burkhardt

Edit� par Springer, 2018

ISBN 10 : 3319882155 ISBN 13 : 9783319882154

Neuf Couverture souple

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Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne

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Machine Learning for the Quantified Self

Mark Hoogendoorn

Edit� par Springer, Springer International Publishing Aug 2018, 2018

ISBN 10 : 3319882155 ISBN 13 : 9783319882154

Neuf Taschenbuch

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783319882154

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Machine Learning for the Quantified Self : On the Art of Learning from Sensory Data

Burkhardt Funk

Edit� par Springer International Publishing, Springer International Publishing, 2018

ISBN 10 : 3319882155 ISBN 13 : 9783319882154

Neuf Taschenbuch

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783319882154

Contacter le vendeur