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  • 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.
  • Sách/Book

  • Introduction to digital speech processing

    • Authors: Rabiner, Lawrence R (2007)

  • Introduction to Digital Speech Processing provides the reader with a practical introduction to the wide range of important concepts that comprise the field of digital speech processing. It serves as an invaluable reference for students embarking on speech research as well as the experienced researcher already working in the field, who can utilize the book as a reference guide.
  • Sách/Book

  • Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

    • Authors: Gallatin, Kyle (2023)

  • This practical guide provides more than 200 self-contained recipes to help you solve Machine Learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems all the way from loading data to training models and leveraging neural networks
  • Sách/Book

  • Laravel: Up & Running Matt Stauffer

    • Authors: Stauffer, Matt (2023)

  • The third edition of this practical guide provides the definitive introduction to one of today's most popular web frameworks.
  • Sách/Book

  • Docker Up & Running: Shipping Reliable Containers in Production

    • Authors: Kane, Sean (2023)

  • This edition includes significant updates to the examples and explanations that reflect the substantial changes that have occurred since Docker was first released almost a decade ago. Sean Kane and Karl Matthias have updated the text to reflect best practices and to provide additional coverage of new features like BuildKit, multi-architecture image support, rootless containers
  • Sách/Book

  • Quantum Care: A Deep Dive into AI for Health Delivery and Research

    • Authors: Mahajan, Rohit (2023)

  • In this book, you will take a deep dive into the remarkable strides that AI is making, with chapters covering: The current and future implementation of AI in healthcare and medicine.The impact of AI in drug discovery with examplesincluding how AI helped bring the COVID-19 vaccines to market. Answers to ethical and privacy concerns about healthcare AI. Best practice guides for practitioners and administrators. A roadmap for startups and investors in healthcare AI
  • Sách/Book

  • Training Data for Machine Learning: Human Supervision from Annotation to Data Science

    • Authors: Sarkis, Anthony (2023)

  • Training Data controls the system by defining the ground truth goals for the creation of Machine Learning models. This involves technical representations, people decisions, processes, tooling, system design, and a variety of new concepts specific to Training Data. In a sense, a Training Data mindset is a paradigm upon which a growing list of theories, research and standards are emerging. A Machine Learning (ML) Model that is created as the end result of a ML Training Process.
  • Sách/Book

  • Compositing visual effects in After effects

    • Authors: Lanier, Lee (2015)

  • In Compositing Visual Effects in After Effects, industry veteran Lee Lanier covers all the common After Effects techniques any serious visual effects artist needs to know, combining the latest, professionally-vetted studio practices and workflows with multi-chapter projects and hands-on lessons.
  • 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.