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  • Sách/Book

  • Blockchain tethered AI : trackable, traceable artifical intelligence and machine learning

    • Authors: Karen Kilyroy (2023)

  • Remove your doubts about AI and explore how this technology can be future-proofed using blockchain's smart contracts and tamper-evident ledgers. With this practical book, system architects, software engineers, and systems solution specialists will learn how enterprise blockchain provides permanent provenance of AI, removes the mystery, and allows you to validate AI before it's ever used. Authors Karen Kilroy, Lynn Riley, and Deepak Bhatta explain that AI's ability to change itself through program synthesis could take the technology beyond human control. With this book, you'll learn an efficient way to solve this problem by building simple blockchain controls for verifying, tracking, tracing, auditing, and even reversing AI. Blockchain tethered AI interweaves the MLOps process with b...
  • Sách/Book

  • Practical Mathematics for AI and Deep Learning

    • 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

  • Explainable Artificial Intelligence in Medical Decision Support Systems

    • 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

  • Designing machine learning systems : an iterative process for production-ready applications

    • 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

  • Quantum Computing: An Applied Approach

    • Authors: Jack D. Hidary (2019)

  • The second edition includes extensive updates and revisions, both to textual content and to the code. Sections have been added on quantum machine learning, quantum error correction, Dirac notation and more