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Results 2811-2820 of 3054 (Search time: 0.187 seconds).
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  • Sách/Book

  • Fundmental mathematical concepts for machine learning in science

    • Authors: Umberto Michelucci (2024)

  • This book is for individuals with a scientific background who aspire to apply machine learning within various natural science disciplinessuch as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous mathematical integrity.
  • Sách/Book

  • Python Machine Learning: A Beginner's Guide to Scikit-Learn

    • Authors: Rajender Kumar (2023)

  • This book is perfect for beginners who are new to machine learning and want to learn Scikit-Learn from scratch. It is also ideal for intermediate and advanced users who want to expand their knowledge and build more complex models.
  • Sách/Book

  • Learn Java fundamentals : a primer for Java development and programming

    • Authors: Jeff Friesen (2024)

  • Sharpen your Java skills and boost your potential as an IT specialist. This book introduces you to the basic Java features and APIs needed to prepare for a career in programming and development. You'll first receive an introduction to Java and then explore language features ranging from comments though exception/error handling, focusing mainly on language syntax and a few select syntax-related APIs. This constitutes the heart of the book, and you'll use these building blocks to construct simple Java programs, and learn where Java's implementations of expressions (and operators), and statements diverge from other languages.
  • Sách/Book

  • Distributed machine learning patterns

    • Authors: Yuan Tang (2024)

  • Inside Distributed Machine Learning Patterns you'll learn to apply established distributed systems patterns to machine learning projects--plus explore cutting-edge new patterns created specifically for machine learning. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Hands-on projects and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines
  • Sách/Book

  • Mastering Dart and Java programming: A Comprehensive Guide for Intermediate Developers - 2 Book in 1/ JP Petersen

    • Authors: Khan, Nikhat Raza (2024)

  • In this comprehensive ebook, you will embark on a journey through the world of Java, exploring the latest features and cutting-edge frameworks that are reshaping the way developers build software. Starting with a solid foundation in core Java concepts, you will quickly progress to more advanced topics, gaining a deeper understanding of Java's capabilities and harnessing its full potential.Discover the magic of Java frameworks, including the ever-popular Spring Framework, which empowers you to build enterprise-level applications effortlessly. Dive into Hibernate ORM, and learn how to effortlessly map Java objects to relational databases. With the help of Apache Maven, you will streamline your project's build and dependency management, saving time and effort.
  • Sách/Book

  • Machine Learning with Python: Foundations and Applications

    • Authors: Mohammed Nurudeen (2024)

  • This book, Machine Learning with Python: Foundations and Applications, is designed to offer a comprehensive introduction to machine learning using Python. The primary goal is to take readers from the fundamental concepts of machine learning to hands-on practical implementations using real-world examples. Python is the language of choice due to its extensive libraries, simplicity, and relevance in the data science community.
  • Sách/Book

  • Signal processing and machine learning theory

    • Authors: Ramirez Diniz; Paulo Sergio (2024)

  • Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory. These theories and tools are the driving engines of many current and emerging research topics and technologies, such as machine learning, autonomous vehicles, the internet of things, future wireless communications, medical imaging, etc
  • Sách/Book

  • Probability and statistics for machine learning : a textbook

    • Authors: Charu C. Aggarwal (2024)

  • The style of writing promotes the learning of probability and statistics simultaneously with a probabilistic perspective on the modeling of machine learning applications. The book contains over 200 worked examples in order to elucidate key concepts. Exercises are included both within the text of the chapters and at the end of the chapters. The book is written for a broad audience, including graduate students, researchers, and practitioners