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  • Sách/Book

  • Representation in Machine Learning

    • Authors: M. N. Murty (2023)

  • This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data.
  • Sách/Book

  • Machine Learning for High-Risk Applications: Approaches to Responsible AI

    • Authors: Hall, Patrick (2023)

  • This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.
  • Sách/Book

  • Training Data for Machine Learning: Human Supervision from Annotation to Data Science

    • Authors: Sarkis, Anthony (2023)

  • Training Data controls the system by defining the ground truth goals for the creation of Machine Learning models. This involves technical representations, people decisions, processes, tooling, system design, and a variety of new concepts specific to Training Data. In a sense, a Training Data mindset is a paradigm upon which a growing list of theories, research and standards are emerging. A Machine Learning (ML) Model that is created as the end result of a ML Training Process.