Machine Learning for the Quantified Self is popular PDF and ePub book, written by Mark Hoogendoorn in 2017-09-28, it is a fantastic choice for those who relish reading online the Technology & Engineering genre. Let's immerse ourselves in this engaging Technology & Engineering book by exploring the summary and details provided below. Remember, Machine Learning for the Quantified Self can be Read Online from any device for your convenience.
Machine Learning for the Quantified Self Book PDF Summary
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.
Detail Book of Machine Learning for the Quantified Self PDF
- Author : Mark Hoogendoorn
- Release : 28 September 2017
- Publisher : Springer
- ISBN : 9783319663081
- Genre : Technology & Engineering
- Total Page : 239 pages
- Language : English
- PDF File Size : 21,9 Mb
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