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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.
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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
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Authors: Karl Kurbel (2013) - This book is about running modern industrial enterprises with the help of information systems.Enterprise resource planning (ERP) is the core of business information processing. An ERP system is the backbone of most companies' information systems landscape. All major business processes are handled with the help of this system.
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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.
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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.
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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.
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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
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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.
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Authors: Mayuri Mehta (2023) - This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas.
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Authors: Pam Baker (2025) - Generative AI tools capable of creating text, images, and even ideas seemingly out of thin air have exploded in popularity and sophistication. This valuable technology can assist in authoring short and long-form content, producing audio and video, serving as a research assistant, and tons of other professional and personal tasks. Generative AI For Dummies is your roadmap to using the world of artificial intelligence to enhance your personal and professional lives. You'll learn how to identify the best platforms for your needs and write the prompts that coax out the content you want. Written by the best-selling author of ChatGPT For Dummies, this book is the ideal place to start when you're ready to fully dive into the world of generative AI.
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