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Marcella Albert J.: Auditing Artificial
Intelligence

Auditing Artificial Intelligence

A Handbook for Audit, Risk, and Security Professionals

Applications in R and Python

Essentials of Big Data Analytics

Studies in National Governance and Emerging Technologies

This book examines how artificial intelligence systems and their applications in security and defence are entangled with governance and policy processes in the EU. Combining concepts and insights from (critical) security studies and science and technology studies, it argues that behind the various AI-related policy initiatives in the EU are particular narratives on security and defence based on European fears of technological innovation and strategic gaps. In this respect, the book casts light on how such beliefs have become increasingly instrumentalized in the European governance dynamics, and subsequently embedded in successive policy initiatives and security and defence research programmes. It challenges conventional understandings of AI technologies as mere security and defence-enabling instruments, by providing a conceptually driven analysis of how certain visions of AI help govern and control one of the most dynamic emergent socio-technical fields. The book will appeal to all those interested in artificial intelligence, security studies, technology and European governance.&nbsp; Raluca Csernatoni is a Research Fellow at Carnegie Europe, where she works on the nexus between European defence and emerging technologies. She is also Guest Professor at the Centre for Security, Diplomacy and Strategy (CSDS), Vrije Universiteit Brussel, Belgium, and Visiting Faculty in the Department of International Relations, Central European University, Austria.

Artificial Security and the Politics of Hype in the EU

Plan and deliver successful events with this practical textbook of essential theory and practice for students studying event planning and management.

Principles, Planning and Practice

Event Planning and Management

Modern Trends In Financial Engineering

Multiscale Financial Data Analytics and Machine Learning offers a systematic and comprehensive study on the multiscale approach to financial data analytics and machine learning. This book covers an array of multiscale methods to discover the properties of various timescales embedded in a financial time series, including noise-assisted empirical mode decomposition methods. Important interpretable multiscale outputs from the estimation are recognized as a new set of features that can be used for machine learning. The feature selection problem for machine learning is examined in this volume.This book offers an applied quantitative approach that combines novel analytical methodologies and practical applications to a wide array of examples with real-world data. It is self-contained and organized in its presentation. The explanations of the methodologies are both accessible and detailed enough to capture the interest of the curious student or researcher. Step-by-step descriptions of the algorithms are provided for straightforward implementation.

Multiscale Financial Data Analytics And Machine Learning

Routledge Studies in Probation and Community Sanctions

Making, Unmaking, Remaking

Probation Governance, Identity, and Practice

Hybrid Computational Intelligence for Pattern Analysis and Understanding

Applications in Data Exploration

Multilevel Quantum Metaheuristics

Sybex Study Guide

The fastest and most effective way to learn the fundamentals of Azure cloud technology and prepare for the AZ-900 certification exam The newly updated second edition of Microsoft Certified Azure Fundamentals Study Guide is an accurate and up-to-date roadmap to the AZ-900 certification exam and a reliable, effective resource for everyone seeking to learn about or reinforce their knowledge of Azure services, pricing, security and compliance, resource management, and more. In the book, experienced Microsoft Principal Customer Success Account Manager and long-time author, Jim Boyce, covers every foundational Azure competency, including: <ul> <li>General and foundational cloud concepts</li> <li>Azure Core Services, including architecture, subscription and billing options, data services, and virtual machines</li> <li>Azure Core Networking Services, like virtual networks, load balancers, and VPN gateways</li> <li>Security, compliance, privacy, and trust features</li> <li>Azure Solutions, like AI and machine learning, serverless computing, and IoT</li> <li>Azure pricing, service levels, and lifecycles</li> <li>Azure Resource creation and management</li> </ul> Ideal for technical and non-technical professionals alike, this straightforward Study Guide draws on the proven Sybex model to explain core Azure features and concepts. It will get you ready to succeed on the widely used AZ-900 certification test and elevate your understanding of Azure cloud software, improving your day-to-day performance in any role that directly or indirectly intersects with the cloud platform. Inside the book <ul> <li>End-of-chapter review questions, with answers and explanations, you can use to measure your progress and help you focus on the areas where you need to improve</li> <li>Concise and intuitive explanations of core Azure services, functions, and features</li> <li>Complimentary access to the superior Sybex online learning environment and test bank, including practice test questions, digital flashcards, and a glossary of key terms</li> </ul> With authoritative, concise coverage of all relevant cloud concepts, Azure architecture and services, and management and governance features tested by the AZ-900 certification exam, this edition of Microsoft Certified Azure Fundamentals Study Guide is the fastest, most efficient way to get up to speed on foundational Azure cloud competencies.

Exam AZ-900

MC Azure Fundamentals Study Guide

Driving Business Success and Profit Through Enterprise Automation and AI Agents

Untangling AI

Psychophysics and Experimental Phenomenology of Pattern Cognition

Retrieval-Augmented Generation (RAG) represents the cutting edge of AI innovation, bridging the gap between large language models (LLMs) and real-world knowledge. This book provides the definitive roadmap for building, optimizing, and deploying enterprise-grade RAG systems that deliver measurable business value.
This comprehensive guide takes you beyond basic concepts to advanced implementation strategies, covering everything from architectural patterns to production deployment. You'll explore proven techniques for document processing, vector optimization, retrieval enhancement, and system scaling, supported by real-world case studies from leading organizations.
Key Learning Objectives
<ul>
<li>Design and implement production-ready RAG architectures for diverse enterprise use cases</li>
<li>Master advanced retrieval strategies including graph-based approaches and agentic systems</li>
<li>Optimize performance through sophisticated chunking, embedding, and vector database techniques</li>
<li>Navigate the integration of RAG with modern LLMs and generative AI frameworks</li>
<li>Implement robust evaluation frameworks and quality assurance processes</li>
<li>Deploy scalable solutions with proper security, privacy, and governance controls</li>
</ul>
Real-World Applications
<ul>
<li>Intelligent document analysis and knowledge extraction</li>
<li>Code generation and technical documentation systems</li>
<li>Customer support automation and decision support tools</li>
<li>Regulatory compliance and risk management solutions</li>
</ul>
Whether you're an AI engineer scaling existing systems or a technical leader planning next-generation capabilities, this book provides the expertise needed to succeed in the rapidly evolving landscape of enterprise AI.
What You Will Learn
<ul>
<li>Architecture Mastery: Design scalable RAG systems from prototype to enterprise production</li>
<li>Advanced Retrieval: Implement sophisticated strategies, including graph-based and multi-modal approaches</li>
<li>Performance Optimization: Fine-tune embedding models, vector databases, and retrieval algorithms for maximum efficiency</li>
<li>LLM Integration: Seamlessly combine RAG with state-of-the-art language models and generative AI frameworks</li>
<li>Production Excellence: Deploy robust systems with monitoring, evaluation, and continuous improvement processes</li>
<li>Industry Applications: Apply RAG solutions across diverse sectors, including finance, healthcare, legal, and technology</li>
</ul>

Auditing Artificial Intelligence

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