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  • Handbook of Research on Artificial Intelligence, Innovation and Entrepreneurship

    • Authors: Carayannis, Elias G (2023)

  • The Handbook of Research on Artificial Intelligence, Innovation and Entrepreneurship focuses on theories, policies, practices, and politics of technology innovation and entrepreneurship based on Artificial Intelligence (AI). It examines when, where, how, and why AI triggers, catalyzes, and accelerates the development, exploration, exploitation, and invention feeding into entrepreneurial actions that result in innovation success.
  • 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"--
  • Sách/Book

  • AI metaheuristics for information security in digital media

    • Authors: Shastri, Apoorva S (2023)

  • This book examines the latest developments in Artificial Intelligence (AI)-based metaheuristics algorithms with applications in information security for digital media. It highlights the importance of several security parameters, their analysis, and validations for different practical applications.
  • Sách/Book

  • Confessions of an AI Brain

    • Authors: Fersman, Elena (2023)

  • The book is written as a first person narrative, from an AI perspective, having the AI brain tell the story.
  • 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.