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.
This provocative study proves the existence of a de facto Confederate policy of giving no quarter to captured black combatants during the Civil War—killing them instead of treating them as prisoners of war. Rather than looking at the massacres as a series of discrete and random events, this work examines each as part of a ruthless but standard practice. Author George S. Burkhardt details a fascinating case that the Confederates followed a consistent pattern of murder against the black soldiers who served in Northern armies after Lincoln’s 1863 Emancipation Proclamation. He shows subsequent retaliation by black soldiers and further escalation by the Confederates, including the execution of some captured white Federal soldiers, those proscribed as cavalry raiders, foragers, or house-burners, and even some captured in traditional battles. Further disproving the notion of Confederates as victims who were merely trying to defend their homes, Burkhardt explores the motivations behind the soldiers’ actions and shows the Confederates’ rage at the sight of former slaves—still considered property, not men—fighting them as equals on the battlefield. Burkhardt’s narrative approach recovers important dimensions of the war that until now have not been fully explored by historians, effectively describing the systemic pattern that pushed the conflict toward a black flag, take-no-prisoners struggle.
This is a relationship recovery guidebook that teaches couples who are currently experiencing damage and dysfunction in their marriage how to have healthy and functioning marriages. This is an excellent book for those who are dealing with abuse, addiction, co-dependency, divorce, illness, infidelity, love addiction, low self-esteem, sexual assault and more. We teach people how to work through the darkest times of their life to achieve love and happiness.
As an integral part of end-to-end process integration, this book provides both experienced and new SAP XI developers with a detailed overview of the functions and usage options of the SAP Exchange Infrastructure. First, readers learn about the central challenges and problems involved with business information systems integration and uncover the extensive options and functions provided by SAP XI to resolve these issues - with a special emphasis on the importance of SAP XI in ESA scenarios. Then follow the authors as they take you deep into the system with a series of practical exercises for the development and configuration of mappings, adapters, and proxies: RFC-to-File, File-to-IDoc, ABAP-Proxy-to-SOAP, and Business Process Management. Each exercise is rounded off by a description of relevant monitoring aspects. A step-by-step introduction to all the individual interface technologies used, which are then combined in a comprehensive case study - part of which is devoted to SAP's BPM Technology - makes this book an invaluable resource. Save time and avoid costly errors in your daily work using the detailed instructions on how best to handle necessary development and configuration objects of SAP XI, plus much more.
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.
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