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Machine Learning for the Quantified Self

2017-09-28 Technology & Engineering
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Release : 2017-09-28
Genre : Technology & Engineering
Kind : eBook
Book Rating : 089/5 ( reviews)

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Book Synopsis Machine Learning for the Quantified Self by : Mark Hoogendoorn

Download or read book Machine Learning for the Quantified Self written by Mark Hoogendoorn. This book was released on 2017-09-28. Available in PDF, EPUB and Kindle. Book excerpt: 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.

An AI for My Life: Exploring the Consequences of Quantified-Self and Machine Learning from a Human Perspective

2019
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Release : 2019
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Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis An AI for My Life: Exploring the Consequences of Quantified-Self and Machine Learning from a Human Perspective by : Lucas Rosenblatt

Download or read book An AI for My Life: Exploring the Consequences of Quantified-Self and Machine Learning from a Human Perspective written by Lucas Rosenblatt. This book was released on 2019. Available in PDF, EPUB and Kindle. Book excerpt:

The Quantified Self in Precarity

2017-09-11 Business & Economics
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Release : 2017-09-11
Genre : Business & Economics
Kind : eBook
Book Rating : 604/5 ( reviews)

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Book Synopsis The Quantified Self in Precarity by : Phoebe V. Moore

Download or read book The Quantified Self in Precarity written by Phoebe V. Moore. This book was released on 2017-09-11. Available in PDF, EPUB and Kindle. Book excerpt: Humans are accustomed to being tool bearers, but what happens when machines become tool bearers, calculating human labour via the use of big data and people analytics by metrics? The Quantified Self in Precarity highlights how, whether it be in insecure ‘gig’ work or office work, such digitalisation is not an inevitable process – nor is it one that necessarily improves working conditions. Indeed, through unique research and empirical data, Moore demonstrates how workplace quantification leads to high turnover rates, workplace rationalisation and worker stress and anxiety, with these issues linked to increased rates of subjective and objective precarity. Scientific management asked us to be efficient. Now, we are asked to be agile. But what does this mean for the everyday lives we lead? With a fresh perspective on how technology and the use of technology for management and self-management changes the ‘quantified’, precarious workplace today, The Quantified Self in Precarity will appeal to undergraduate and postgraduate students interested in fields such as Science and Technology, Organisation Management, Sociology and Politics.

The Qualified Self

2018-04-20 Social Science
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Release : 2018-04-20
Genre : Social Science
Kind : eBook
Book Rating : 265/5 ( reviews)

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Book Synopsis The Qualified Self by : Lee Humphreys

Download or read book The Qualified Self written by Lee Humphreys. This book was released on 2018-04-20. Available in PDF, EPUB and Kindle. Book excerpt: How sharing the mundane details of daily life did not start with Facebook, Twitter, and YouTube but with pocket diaries, photo albums, and baby books. Social critiques argue that social media have made us narcissistic, that Facebook, Twitter, Instagram, and YouTube are all vehicles for me-promotion. In The Qualified Self, Lee Humphreys offers a different view. She shows that sharing the mundane details of our lives—what we ate for lunch, where we went on vacation, who dropped in for a visit—didn't begin with mobile devices and social media. People have used media to catalog and share their lives for several centuries. Pocket diaries, photo albums, and baby books are the predigital precursors of today's digital and mobile platforms for posting text and images. The ability to take selfies has not turned us into needy narcissists; it's part of a longer story about how people account for everyday life. Humphreys refers to diaries in which eighteenth-century daily life is documented with the brevity and precision of a tweet, and cites a nineteenth-century travel diary in which a young woman complains that her breakfast didn't agree with her. Diaries, Humphreys explains, were often written to be shared with family and friends. Pocket diaries were as mobile as smartphones, allowing the diarist to record life in real time. Humphreys calls this chronicling, in both digital and nondigital forms, media accounting. The sense of self that emerges from media accounting is not the purely statistics-driven “quantified self,” but the more well-rounded qualified self. We come to understand ourselves in a new way through the representations of ourselves that we create to be consumed.

Automated Machine Learning

2019-05-17 Computers
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Release : 2019-05-17
Genre : Computers
Kind : eBook
Book Rating : 180/5 ( reviews)

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Book Synopsis Automated Machine Learning by : Frank Hutter

Download or read book Automated Machine Learning written by Frank Hutter. This book was released on 2019-05-17. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

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