Discount Quality For Responsible Data Science : Human-In-The-Loop For Quality Data Barbara Pernici
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Discount Quality for Responsible Data ScienceHuman-in-the-Loop for Quality Data\nAuthor(s): Barbara Pernici, Tiziana Catarci, Matteo Palmonari, Giovanni Simonini\nFormat: Paperback\nPublisher: Springer Nature Switzerland AG, Switzerland\nImprint: Springer Nature Switzerland AG\nISBN-13: 9783032294807, 978-3032294807\nSynopsis\nThisopen accessbook presentsa discount-quality approach to data preparation for scientific data analysis, a critical prerequisite in modern applications such as data analysis projects, Artificial Intelligence, and Machine Learning. It discusses advanced techniques for fostering responsible data science, [url] designing sustainable data analysis pipelines based on Human-In-The-Loop (HITL) approaches to achieve high-quality data. It investigatesdeveloping task- and context-driven sustainable approaches for data preparation, drawing on methods and theories to reduce annotations and processing space and time, and considering the estimation of the necessary human co.
Discount Quality For Responsible Data Science : Human-In-The-Loop For Quality Data Barbara Pernici
Discount Quality for Responsible Data ScienceHuman-in-the-Loop for Quality Data\nAuthor(s): Barbara Pernici, Tiziana Catarci, Matteo Palmonari, Giovanni Simonini\nFormat: Paperback\nPublisher: Springer Nature Switzerland AG, Switzerland\nImprint: Springer Nature Switzerland AG\nISBN-13: 9783032294807, 978-3032294807\nSynopsis\nThisopen accessbook presentsa discount-quality approach to data preparation for scientific data analysis, a critical prerequisite in modern applications such as data analysis projects, Artificial Intelligence, and Machine Learning. It discusses advanced techniques for fostering responsible data science, [url] designing sustainable data analysis pipelines based on Human-In-The-Loop (HITL) approaches to achieve high-quality data. It investigatesdeveloping task- and context-driven sustainable approaches for data preparation, drawing on methods and theories to reduce annotations and processing space and time, and considering the estimation of the necessary human co.
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