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  • Sách/Book

  • MLOps with Ray : best practices and strategies for adopting machine learning operations

    • Authors: Hien Luu (2024)

  • This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps. What You'll Learn Gain an understanding of the MLOps discipline Know the MLOps technical stack and its components Get familiar with the MLOps adoption strategy Understand feature engineering Who This Book Is For Machine learning practitioners, data scientists, and software engineers who are focusing on building machine learning systems and infrastructure to bring ML models to production
  • Sách/Book

  • Beginning Blockchain : a beginner's guide to building Blockchain solutions

    • Authors: Bikramaditya Singhal (2018)

  • Understand the nuts and bolts of Blockchain, its different flavors with simple use cases, and cryptographic fundamentals. You will also learn some design considerations that can help you build custom solutions. Beginning Blockchain is a beginner's guide to understanding the core concepts of Blockchain from a technical perspective. By learning the design constructs of different types of Blockchain, you will get a better understanding of building the best solution for specific use cases. The book covers the technical aspects of Blockchain technologies, cryptography, cryptocurrencies, and distributed consensus mechanisms. You will learn how these systems work and how to engineer them to design next-gen business solutions.
  • Sách/Book

  • Situational modelig : definitions, awareness, simulation

    • Authors: Alexander Fridman (2024)

  • "This book introduces a methodology of situational modeling and control for discrete spatial dynamic systems based on thorough implementation of situational control by D.A. Pospelov and the theory of hierarchical systems by M.D. Mesarovic. This methodology provides flexible intelligent analysis and structural synthesis of such systems, including searches for "bottlenecks" in their structure that can cause the most harmful losses in emergencies"
  • Sách/Book

  • Machine learning crash course for engineers

    • Authors: Eklas Hossain (2024)

  • The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement machine learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of machine learning quickly.
  • Sách/Book

  • Machine learning for network traffic and video quality analysis : develop and deploy applications using JavaScript and Node.js

    • Authors: Tulsi Pawan Fowdur (2024)

  • This book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using JavaScript. JavaScript provides the flexibility to deploy these applications across various devices and web browsers. The book begins by delving into NTMA, explaining fundamental concepts and providing an overview of existing applications and research within this domain. It also goes into the essentials of VQA and offers a survey of the latest developments in VQA algorithms.
  • Sách/Book

  • Principles of data science : a beginner's guide to essential math and coding skills for data fluency and machine learning

    • Authors: Sinan Ozdemir (2024)

  • Throughout the book, you'll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data. With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You'll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift.
  • Sách/Book

  • Graph-Powered Analytics and Machine Learning with TigerGraph

    • Authors: Victor Lee (2022)

  • With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available. You'll explore a three-stage approach to deriving value from connected data: connect, analyze, and learn.
  • Sách/Book

  • Mastering Design Patterns in Java: Essential Skills/ Ed Norex

    • Authors: - (2024)

  • Trang bị cho người đọc kiến thức sâu rộng về các mẫu thiết kế, đươc thiết kế riêng cho môi trường lập trình Java, cho phép họ giải quyết thách thức phần mềm phức tạp một cách tự tin và hiệu quả.
  • Sách/Book

  • How machine learning is innovating today's world : a concise technical guide

    • Authors: Arindam Dey (2024)

  • Provides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques. Machine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems. As researchers and practitioners in the field, the editors of this book recognize the importance of disseminating knowledge and fostering collaboration to further advance this dynamic discipline. How Machine Learning is Innovating Today's World is a timely book and presents a diverse collection of 25 chapters that delve into the remarkable ways that ML is transforming various fields and industries.