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Machine Learning for the Quantified Self

On the Art of Learning from Sensory Data

Language EnglishEnglish
Book Paperback
Book Machine Learning for the Quantified Self Mark Hoogendoorn
Libristo code: 21376048
Publishers Springer International Publishing AG, August 2018
This book explains the complete loop to effectively use self-tracking data for machine learning. Whi... Full description
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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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About the book

Full name Machine Learning for the Quantified Self
Language English
Binding Book - Paperback
Date of issue 2018
Number of pages 231
EAN 9783319882154
Libristo code 21376048
Weight 385
Dimensions 155 x 235 x 14
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