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dc.contributor.authorYada Pruksachatkun-
dc.date.accessioned2024-03-13T08:00:15Z-
dc.date.available2024-03-13T08:00:15Z-
dc.date.issued2023-
dc.identifier.urihttp://thuvienso.thanglong.edu.vn//handle/TLU/9556-
dc.description.abstractWith the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile worldvi
dc.language.isoenvi
dc.publisherO'Reilly Mediavi
dc.subjectArtificial intelligence | Algorithms | Data mining | Machine learning | Khai thác dữ liệu | Trí tuệ nhân tạovi
dc.titlePracticing trustworthy machine learning : consistent, transparent, and fair AI pipelinesvi
dc.typeSách/Bookvi
Appears in Collections1-Trí tuệ nhân tạo

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