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Results 2781-2790 of 3054 (Search time: 0.538 seconds).
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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

  • Learning Google Cloud Vertex AI

    • Authors: Hemanth Kumar K (2023)

  • Google Cloud Vertex AI is a platform for machine learning (ML) offered by Google Cloud, with the objective of making the creation, deployment, and administration of ML models on a large scale easier. If you are seeking a unified and collaborative environment for your ML projects, this book is a valuable resource for you. This comprehensive guide is designed to help data enthusiasts effectively utilize Google Cloud Platform's Vertex AI for a wide range of machine learning operations. It covers the basics of the Google Cloud Platform, encompassing cloud storage, big query, and IAM. Subsequently, it delves into the specifics of Vertex AI, including AutoML, custom model training, model deployment on endpoints, development of Vertex AI pipelines, and the Explainable AI feature store. By ...
  • Sách/Book

  • Advanced Machine Learning with Evolutionary and Metaheuristic Techniques

    • Authors: Jayaraman Valadi (2024)

  • The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning. It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization and machine learning, paving the way for pioneering innovations in the field
  • 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

  • Artificial intelligence and machine learning techniques in image processing and computer vision

    • Authors: Karm Veer Arya (2024)

  • "Artificial Intelligence and Machine Learning Techniques in Image Processing and Computer Vision provides in-depth and detailed knowledge about the latest research in image processing and computer vision techniques. It is a roadmap for the improvement of computer vision and image processing, explaining the machine learning algorithms and models involved. The authors differentiate between the various algorithms available and how to choose which to use for the most precise results for a specific task involving certain constraints. The volume provides real-world examples to illustrate the concepts and methods. The authors discuss machine learning in healthcare systems for detection, diagnosis, classification, and segmentation. They also explore the diverse applications of image and vid...
  • Sách/Book

  • Statistical prediction and machine learning

    • Authors: John Tuhao Chen (2024)

  • "Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms, and range regressions. After a discussion on unsupervised learning methods, it includes a chapter ...