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    Showing results 9 to 13 of 13
    • TVS.006030_TT_Patrick Hall, James Curtis, and Parul Pandey - Machine Learning for High-Risk Applications_ Techniques for Responsible AI (11th Early Re.pdf.jpg
    • 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.
    • TVS.005001_TT_(The Python Series) Stephen Lynch - Python for Scientific Computing and Artificial Intelligence-CRC Press (2023).pdf.jpg
    • Sách/Book

    • Python for scientific computation and artificial intelligence

      • Authors: Lynch, Stephen (2023)

    • This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling. Features: No prior experience of programming is required. Online GitHub repository available with codes for readers to practice. Covers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computing. Full solutions to exercises are available as Jupyter notebooks on the Web"--