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Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data: 35 (Cognitive Systems Monographs, 35) - Softcover
Book 22 of 31: Cognitive Systems Monographs
Synopsis
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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From the Back Cover
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 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 sample 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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Hoogendoorn:Machine Learning for the Qu (eng)
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Machine Learning for the Quantified Self
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ISBN 10: 3319882155
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Condition: 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. Seller Inventory # 448761342
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Machine Learning for the Quantified Self
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ISBN 10: 3319882155
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Taschenbuch. Condition: 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. Seller Inventory # 9783319882154
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Machine Learning for the Quantified Self | On the Art of Learning from Sensory Data
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Taschenbuch. Condition: Neu. Machine Learning for the Quantified Self | On the Art of Learning from Sensory Data | Mark Hoogendoorn (u. a.) | Taschenbuch | xv | Englisch | 2018 | Springer | EAN 9783319882154 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 115379384
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Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data: 35 (Cognitive Systems Monographs)
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Machine Learning for the Quantified Self
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Taschenbuch. Condition: 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. Seller Inventory # 9783319882154
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Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data: 35 (Cognitive Systems Monographs)
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Machine Learning for the Quantified Self : On the Art of Learning from Sensory Data
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Taschenbuch. Condition: 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. Seller Inventory # 9783319882154
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Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data (Cognitive Systems Monographs, 35)
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