| 000 -LEADER |
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| fixed length control field |
nam a22 7a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
180413b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783319663074 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
HOO |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Hoogendoorn, Mark |
| 245 ## - TITLE STATEMENT |
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| Title |
Machine learning for the quantified Self : on the art of learning from sensory data |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Springer, |
| Date of publication, distribution, etc |
2018 |
| Place of publication, distribution, etc |
Switzerland: |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xv, 231 p. |
| Dimensions |
25 cm. |
| 365 ## - TRADE PRICE |
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| Price type code |
EURO |
| Price amount |
129.99/ Rs. 10893.16 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Convolutional Neural Networks |
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| Topical term or geographic name as entry element |
Sensory data |
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| Topical term or geographic name as entry element |
Data terminology |
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| Topical term or geographic name as entry element |
Mathematical foundation |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Funk, Burkhardt |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
|
| Item type |
Books |