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Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data (Cognitive Systems Monographs)
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Bester Preis: € 5,55 (vom 12.09.2019)Machine Learning for the Quantified Self
ISBN: 9783319663081 bzw. 3319663089, vermutlich in Englisch, Springer Shop, neu, E-Book, elektronischer Download.
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. eBook.
Machine Learning for the Quantified Self - On the Art of Learning from Sensory Data
ISBN: 9783319663074 bzw. 3319663070, in Deutsch, Springer-Verlag Gmbh, gebundenes Buch, neu.
Machine Learning for the Quantified Self: 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. Englisch, Buch.
Machine Learning for the Quantified Self (2017)
ISBN: 9783319663081 bzw. 3319663089, in Englisch, Springer, Springer, Springer, neu, E-Book, elektronischer Download.
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.
Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data (Cognitive Systems Monographs) (2017)
ISBN: 9783319663081 bzw. 3319663089, in Englisch, 231 Seiten, Springer, neu, Erstausgabe, E-Book, elektronischer Download.
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. Kindle Edition, Ausgabe: 1st ed. 2018, Format: Kindle eBook, Label: Springer, Springer, Produktgruppe: eBooks, Publiziert: 2017-11-20, Freigegeben: 2017-11-20, Studio: Springer.
Machine Learning for the Quantified Self als von Mark Hoogendoorn, Burkhardt Funk
ISBN: 9783319663074 bzw. 3319663070, in Deutsch, gebundenes Buch, neu.
Machine Learning for the Quantified Self ab 85.49 EURO On the Art of Learning from Sensory Data Cognitive Systems Monographs, Machine Learning for the Quantified Self ab 85.49 EURO On the Art of Learning from Sensory Data Cognitive Systems Monographs.
Machine Learning for the Quantified Self (2017)
ISBN: 9783319663081 bzw. 3319663089, in Englisch, Springer, Springer, Springer, neu, E-Book, elektronischer Download.
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.
Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data (Cognitive Systems Monographs) (2017)
ISBN: 9783319663074 bzw. 3319663070, in Englisch, 226 Seiten, Springer, gebundenes Buch, neu, Erstausgabe.
Von Händler/Antiquariat, Amazon.de.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Machine Learning for the Quantified Self
ISBN: 9783319663081 bzw. 3319663089, in Deutsch, Springer Science+Business Media, neu, E-Book.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen