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  • Sách/Book


  • Authors: Tamoghna Ghosh (2022)

  • This book will teach you the common terminologies used in artificial intelligence such as models, data, parameters of models, and dependent and independent variables.

  • Sách/Book


  • Authors: Agbotiname Lucky Imoize (2023)

  • This book discusses XAI-based analytics for patient-specific MDSS as well as related security and privacy issues associated with processing patient data. It provides insights into real-world scenarios of the deployment, application, management, and associated benefits of XAI in MDSS.

  • Sách/Book


  • Authors: Chip Huyen (2022)

  • Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives

  • Sách/Book


  • Authors: Paula Boddington (2023)

  • This book introduces readers to critical ethical concerns in the development and use of artificial intelligence. Offering clear and accessible information on central concepts and debates in AI ethics, it explores how related problems are now forcing us to address fundamental, age-old questions about human life, value, and meaning. In addition, the book shows how foundational and theoretical issues relate to concrete controversies, with an emphasis on understanding how ethical questions play out in practice.

  • Sách/Book


  • Authors: Siddhartha Bhattacharyya (2023)

  • This book provides a detailed insight into Robotic Process Automation (RPA) technologies linked with AI that will help organizations implement Industry 4.0 procedures. RPA tools enhance their functionality by incorporating AI objectives, such as use of artificial neural network algorithms, text mining techniques, and natural language processing techniques for information extraction and the subsequent process of optimization and forecasting scenarios for the purpose of improving an organization's operational and business processes.

  • Sách/Book


  • Authors: Chirag Paunwala (2023)

  • This book focuses on advanced techniques used for feature extraction, analysis, recognition, and classification in the area of biomedical signal and image processing. Contributions cover all aspects of artificial intelligence, machine learning, and deep learning in the field of biomedical signal and image processing using novel and unexplored techniques and methodologies.