Téléchargez le livre : Machine Learning for the Quantified Self

Machine Learning for the Quantified Self

On the Art of Learning from Sensory Data de

,

Éditeur :

Springer


Collection :

Cognitive Systems Monographs

Paru le : 2017-09-28

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Description
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.
Pages
231 pages
Collection
Cognitive Systems Monographs
Parution
2017-09-28
Marque
Springer
EAN papier
9783319663074
EAN PDF
9783319663081
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
23
Taille du fichier
13875 Ko
Prix
168,79 €
EAN EPUB
9783319663081
Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
23
Taille du fichier
3410 Ko
Prix
168,79 €

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