Stock photo: cover may vary
MACHINE LEARNING FOR THE QUANTIFIED SELFON THE ART OF LEARNING FROM SENSORY DATA Other - 2017
by Mark Hoogendoorn; Burkhardt Funk
- New
A$410.82
A$5.82
Delivery within USA
Standard delivery: 9 to 14 days
More delivery options
Standard delivery: 9 to 14 days
Ships from Cold Books (New York, United States)
Details
- Title MACHINE LEARNING FOR THE QUANTIFIED SELFON THE ART OF LEARNING FROM SENSORY DATA
- Author Mark Hoogendoorn; Burkhardt Funk
- Binding Other
- Condition New
- Pages 231
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2017-10-05
- Illustrated Yes
- Features Illustrated
- Bookseller's Inventory # 6375627837
- ISBN 9783319663074 / 3319663070
- Weight 1.16 lbs (0.53 kg)
- Dimensions 9.21 x 6.14 x 0.63 in (23.39 x 15.60 x 1.60 cm)
- Category Computers - General Information
- Dewey Decimal Code 006.3
- Quantity available 4
About Cold Books New York, United States
Reader reviews for MACHINE LEARNING FOR THE QUANTIFIED SELFON THE ART OF LEARNING FROM SENSORY DATA
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information
From the publisher
From the rear 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.
