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  • Sách/Book


  • 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


  • 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


  • 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


  • 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


  • 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


  • 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.