Browsing by Subject Artificial intelligence

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Showing results 10 to 19 of 19
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  • Authors: - (2024)

  • By focusing on facial expressions, gestures, and body movements, we delve into uncharted territories of research, offering novel methodologies, databases, benchmarks, and algorithms for the analysis of human behavior in natural settings. Geared toward academic scholars, this book compiles the expertise of leading researchers in the field, making it accessible to readers of all educational backgrounds.

  • TVS.005257_TT_Abdulrahman Yarali - From 5G to 6G_ Technologies, Architecture, AI, and Security-Wiley (2023).pdf.jpg
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  • Authors: Yarali, Abdulrahman (2023)

  • The transition from the fifth generation of wireless communication (5G) to the coming sixth generation (6G) promises to be one of the most significant phases in the history of telecommunications. The technological, social, and logistical challenges promise to be significant, and meeting these challenges will determine the future of wireless communication. Experts and professionals across dozens of fields and industries are beginning to reckon seriously with these challenges as the 6G revolution approaches. From 5G to 6G provides an overview of this transition, offering a snapshot of a moment in which 5G is establishing itself and 6G draws ever nearer. It focuses on recent advances in ...

  • TVS.005057_TT_Elias G. Carayannis_ Evangelos Grigoroudis - Handbook of Research on Artificial Intelligence, Innovation and Entrepreneurship-Edward Elg.pdf.jpg
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  • 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.

  • TVS.006011_TT_Hands-On Generative AI with Transformers and Diffusion Models.pdf.jpg
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  • Authors: Cuenca, Pedro (2023)

  • This book introduces theoretical concepts in an intuitive way, with extensive code samples and illustrations that you can run on services such as Google Colaboratory, Kaggle, or Hugging Face Spaces with minimal setup. You'll learn how to use open source libraries such as Transformers and Diffusers, conduct code exploration, and study several existing projects to help guide your work.

  • TVS.005058_TT_Huijue Jia - Neuroscience for Artificial Intelligence-Jenny Stanford Publishing (2023).pdf.jpg
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  • Authors: Jia, Huijue (2023)

  • This is a timely book to introduce the new discoveries and ideas in neuroscience, for the next wave of more powerful AI. AI researchers are all interested in the human brain, which is more capable and energy-efficient, but do not have good reading materials from the rather separate subfields of neuroscience, all with plenty of jargons. Based on hundreds of publications from top journals, the book fills in the gap between existing computational hardware/algorithms and emerging knowledge from neuroscience.

  • TVS.005001_TT_(The Python Series) Stephen Lynch - Python for Scientific Computing and Artificial Intelligence-CRC Press (2023).pdf.jpg
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  • 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"--

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  • Authors: Duke, Toju (2025)

  • The centerpiece of this book is a risk management and assessment framework titled "Safe Human-centered AI (SAFE-HAI)," which highlights AI risks across the following Responsible AI principles: accuracy, sustainability and robustness, explainability, transparency and accountability, fairness, privacy and human rights, human-centered AI, and AI governance.

  • TVS.005276_TT_(Wiley and SAS Business Series) Terisa Roberts, Stephen J. Tonna - Risk Modeling_ Practical Applications of Artificial Intelligence, Mac.pdf.jpg
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  • Authors: Roberts, Terisa (2022)

  • This book provides an overview and introduction to the application of artificial intelligence and machine learning in risk management. It will cover practical application of newer modelling techniques in risk management and explore what the opportunities are of using artificial intelligence and machine learning, as well as the risks and challenges associated with the innovation. In addition, it will explain the options to extend the model governance framework for artificial intelligence and machine learning