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Results 1721-1730 of 1753 (Search time: 0.004 seconds).
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  • Sách/Book

  • Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

    • Authors: Maxim Lapan (2020)

  • Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.
  • Sách/Book

  • Cloud network management : an IoT based framework

    • Authors: Sanjay Kumar Biswash (2021)

  • This book provides a framework for the next generation of cloud networks, which is the emerging part of 5G partnership projects. This contributed book has following salient features, A cloud-based next generation networking technologies. Cloud-based IoT and mobility management technology. The proposed book is a reference for research scholars and course supplement for cloud-IoT related subjects such as distributed networks in computer/ electrical engineering.
  • Sách/Book

  • Data Engineering with Dbt : A Practical Guide to Building a Cloud-Based, Pragmatic, and Dependable Data Platform with SQL

    • Authors: Roberto Zagni (2023)

  • This book begins by introducing you to dbt and its role in the data stack, along with how it uses simple SQL to build your data platform, helping you and your team work better together. You'll find out how to leverage data modeling, data quality, master data management, and more to build a simple-to-understand and future-proof solution. As you advance, you'll explore the modern data stack, understand how data-related careers are changing, and see how dbt enables this transition into the emerging role of an analytics engineer.
  • Sách/Book

  • Distributed and cloud computing : from parallel processing to the Internet of things

    • Authors: Kai Hwang (2012)

  • Complete coverage of modern distributed computing technology including clusters, the grid, service-oriented architecture, massively parallel processors, peer-to-peer networking, and cloud computing. Includes case studies from the leading distributed computing vendors: Amazon, Microsoft, Google, and more. Explains how to use virtualization to facilitate management, debugging, migration, and disaster recovery. Designed for undergraduate or graduate students taking a distributed systems course―each chapter includes exercises and further reading, with lecture slides and more available online
  • Sách/Book

  • Beginning Apache Spark 3 : with DataFrame, Spark SQL, structured streaming, and Spark machine learning library

    • Authors: Hien Luu (2021)

  • In this book, you will gain expertise on the powerful and efficient distributed data processing engine inside of Apache Spark; its user-friendly, comprehensive, and flexible programming model for processing data in batch and streaming; and the scalable machine learning algorithms and practical utilities to build machine learning applications. Beginning Apache Spark 3 begins by explaining different ways of interacting with Apache Spark, such as Spark Concepts and Architecture, and Spark Unified Stack.
  • Sách/Book

  • Cloud computing

    • Authors: Sandeep Bhowmik (2017)

  • This textbook is ideal for undergraduate and graduate students of computer science engineering, and information technology.Presents in-depth coverage on fundamental concepts and essential technologies of cloud computing. Emphasizes popular cloud services and security issues. Contains case studies and emerging trends including internet of things Includes numerous review questions and multiple choice questions for better understanding.
  • Sách/Book

  • Data analytics for finance using Python

    • Authors: Nitin Jaglal Untwal (2025)

  • In this book, you'll discover how to harness the latest data analytics techniques, including machine learning and inferential statistics, to make informed investment decisions and drive business success. With a focus on practical application, this book takes you on a journey from the basics of data preprocessing and visualization to advanced modeling techniques for stock price prediction.
  • Sách/Book

  • Computer security: principles and practice

    • Authors: William Stallings (2018)

  • The objective of this book is to provide an up-to-date survey of developments in computer security. Central problems that confront security designers and security administrators include defining the threats to computer and network systems, evaluating the relative risks of these threats, and developing cost-effective and user friendly countermeasures
  • Sách/Book

  • Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning

    • Authors: Uday Kamath (2021)

  • This book takes an in-depth approach to presenting the fundamentals of explain-able AI through mathematical theory and practical use cases. The content is split into four parts: pre-model methods, intrinsic methods, post-hoc methods, and deep- learning methods. The first part introduces pre-model techniques for Explainable AI (XAI). Part Two presents classical and modern intrinsic model interpretability methods, while Part Three details the collection of post-hoc methods. Part Four dives into methods tailored specifically for deep learning models.