Understand the full potential of Microsoft Power Platform with this comprehensive guide, designed to provide you with the knowledge and tools needed to create intelligent business applications, automate workflows, and drive data-driven insights for business growth. Whether you're a novice or an experienced professional, this book offers a step-by-step approach to mastering the Power Platform. This book comes with an extensive array of essential concepts, architectural patterns and techniques. It will also guide you with practical insights to navigate the Power Platform effortlessly while integrating on Azure. Starting with exploring Power Apps for building enterprise applications, the book delves into Dataverse, Copilot Studio, AI Builder, managing platforms and Application life cycle management. You will then demonstrate testing strategy followed by a detailed examination of Dataverse and intelligent AI-powered Applications. Additionally, you will cover Power pages for external websites and AI-infused solutions. Each section is meticulously structured, offering step-by-step guidance, hands-on exercises, and real-world scenarios to reinforce learning. After reading the book, you will be able to optimize your utilization of the Power Platform for creating effective business solutions. What You Will Learn: Understand the core components and capabilities of Power Platform Explore how Power Platform integrates with Azure services Understand the key features and benefits of using Power Platform for business applications Discover best practices for governance to ensure compliance and efficient management Explore techniques for optimizing the performance of data integration and export processes on Azure Who This Book Is For: Application developers, Enterprise Architects and business decision-makers.

Explore how artificial intelligence can transform your cybersecurity strategies with "Artificial Intelligence for Cybersecurity". This book provides practical insights into applying AI methods to a variety of cybersecurity problems, from malware analysis to threat detection. By understanding these concepts, you'll gain the knowledge needed to protect your organization's data and networks effectively. What this Book will help me do Understand how AI methods can address cybersecurity concerns effectively. Develop practical skills using AI tools to combat cyber threats. Design AI-powered solutions for malware identification and anomaly detection. Navigate real-world applications of AI in cybersecurity scenarios. Recognize and mitigate common pitfalls while implementing AI methods in cybersecurity. Author(s) The authors, Bojan Kolosnjaji, Huang Xiao, Peng Xu, and Apostolis Zarras, are experts in machine learning and cybersecurity. With extensive backgrounds in both academia and industry, they bring a wealth of knowledge to the book. Their practical and educational approach makes complex AI and cybersecurity concepts accessible, empowering readers to apply these methods to real-world problems. Who is it for? This book is ideal for professionals in cybersecurity who are keen to integrate AI techniques into their frameworks and workflows. It's also suitable for machine learning enthusiasts who want to delve into the realm of cybersecurity. If you possess a basic understanding of Python programming and machine learning fundamentals, this book will guide you through to advanced concepts. Whether you are a student or an industry veteran, this book offers valuable insights for enhancing your cybersecurity strategies with AI.

The "LLM Engineer's Handbook" is your comprehensive guide to mastering Large Language Models from concept to deployment. Written by leading experts, it combines theoretical foundations with practical examples to help you build, refine, and deploy LLM-powered solutions that solve real-world problems effectively and efficiently. What this Book will help me do Understand the principles and approaches for training and fine-tuning Large Language Models (LLMs). Apply MLOps practices to design, deploy, and monitor your LLM applications effectively. Implement advanced techniques such as retrieval-augmented generation (RAG) and preference alignment. Optimize inference for high performance, addressing low-latency and high availability for production systems. Develop robust data pipelines and scalable architectures for building modular LLM systems. Author(s) Paul Iusztin and Maxime Labonne are experienced AI professionals specializing in natural language processing and machine learning. With years of industry and academic experience, they are dedicated to making complex AI concepts accessible and actionable. Their collaborative authorship ensures a blend of theoretical rigor and practical insights tailored for modern AI practitioners. Who is it for? This book is tailored for AI engineers, NLP professionals, and LLM practitioners who wish to deepen their understanding of Large Language Models. Ideal readers possess some familiarity with Python, AWS, and general AI concepts. If you aim to apply LLMs to real-world scenarios or enhance your expertise in AI-driven systems, this handbook is designed for you.

The convergence of Artif icial Intelligence (AI) and Internet of Things (IoT) is reshaping the way industries, businesses, and economies function; the 34 chapters in this collection show how the full potential of these technologies is being enabled to create intelligent machines that simulate smart behavior and support decision-making with little or no human interference, thereby providing startling organizational efficiencies. Readers will discover that in Reshaping Intelligent Business and Industry: The book unpacks the two superpowers of innovation, AI and IoT, and explains how they connect to better communicate and exchange information about online activities; How the center and the network's edge generate predictive analytics or anomaly alerts; The meaning of AI at the edge and IoT networks. How bandwidth is reduced and privacy and security are enhanced; How AI applications increase operating efficiency, spawn new products and services, and enhance risk management; How AI and IoT create 'intelligent' devices and how new AI technology enables IoT to reach its full potential; Analyzes AIOT platforms and the handling of personal information for shared frameworks that remain sensitive to customers’ privacy while effectively utilizing data. Audience This book will appeal to all business and organization leaders, entrepreneurs, policymakers, and economists, as well as scientists, engineers, and students working in artificial intelligence, software engineering, and information technology.

Are you grappling with increasing productivity and enhancing creativity within your business processes? As businesses evolve in this digital age, the demand for swift, efficient, and innovative solutions is more pressing than ever. Traditional methods often fall short in keeping pace with the rapid changes and challenges that professionals face daily. Enter this report by Thomas Nield. This curated guide outlines the transformative power of generative AI in various business functions and serves as a much-needed solution to overcoming modern business hurdles. Discover how AI can be your ally in not just meeting but exceeding your productivity and creativity goals. You'll learn how to: Quickly find and use relevant images for presentations, blogs, and articles Save valuable time and refine your communications with AI-assisted email rewriting Easily distill large volumes of information into essential summaries Leverage AI for efficient data-gathering from the web, perfectly suited for analysis Utilize AI-generated text and visuals to craft compelling basic marketing materials

Artificial Intelligence (AI) revolves around creating and utilizing intelligent machines through science and engineering. This book delves into the theory and practical applications of computer science methods that incorporate AI across many domains. It covers techniques such as Machine Learning (ML), Convolutional Neural Networks (CNN), Deep Learning (DL), and Large Language Models (LLM) to tackle complex issues and overcome various challenges.

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code

Pearson’s Artificial Intelligence encompasses a comprehensive text on the fundamental principles and concepts of Artificial Intelligence—a new-age technology that fuels the much-coveted ‘Industry 4.0’. Presented in lucid English, this book covers all the basic concepts, enriched with latest examples. It also discusses all the major components of AI, such as Neural Networks, Natural Language Processing, Reinforcement Learning, Machine Learning, Deep Learning and Computer Vision. The book is a deliberation of classical as well modern AI techniques and related technologies that provides readers with an overall knowledge and understanding of AI in present-day context.

Get practical advice on how to leverage AI development tools for all stages of code creation, including requirements, planning, design, coding, debugging, testing, and documentation. With this book, beginners and experienced developers alike will learn how to use a wide range of tools, from general-purpose LLMs (ChatGPT, Gemini, and Claude) to code-specific systems (GitHub Copilot, Tabnine, Cursor, and Amazon CodeWhisperer). You'll also learn about more specialized generative AI tools for tasks such as text-to-image creation. Author Tom Taulli provides a methodology for modular programming that aligns effectively with the way prompts create AI-generated code. This guide also describes the best ways of using general purpose LLMs to learn a programming language, explain code, or convert code from one language to another. This book examines: The core capabilities of AI-based development tools Pros, cons, and use cases of popular systems such as GitHub Copilot and Amazon CodeWhisperer Ways to use ChatGPT, Gemini, Claude, and other generic LLMs for coding Using AI development tools for the software development lifecycle, including requirements, planning, coding, debugging, and testing Prompt engineering for development Using AI-assisted programming for tedious tasks like creating regular expressions, starter code, object-oriented programming classes, and GitHub Actions How to use AI-based low-code and no-code tools, such as to create professional UIs

APPLYING ARTIFICIAL INTELLIGENCE IN CYBERSECURITY ANALYTICS AND CYBER THREAT DETECTION Comprehensive resource providing strategic defense mechanisms for malware, handling cybercrime, and identifying loopholes using artificial intelligence (AI) and machine learning (ML) Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection is a comprehensive look at state-of-the-art theory and practical guidelines pertaining to the subject, showcasing recent innovations, emerging trends, and concerns as well as applied challenges encountered, and solutions adopted in the fields of cybersecurity using analytics and machine learning. The text clearly explains theoretical aspects, framework, system architecture, analysis and design, implementation, validation, and tools and techniques of data science and machine learning to detect and prevent cyber threats. Using AI and ML approaches, the book offers strategic defense mechanisms for addressing malware, cybercrime, and system vulnerabilities. It also provides tools and techniques that can be applied by professional analysts to safely analyze, debug, and disassemble any malicious software they encounter. With contributions from qualified authors with significant experience in the field, Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection explores topics such as: Cybersecurity tools originating from computational statistics literature and pure mathematics, such as nonparametric probability density estimation, graph-based manifold learning, and topological data analysis Applications of AI to penetration testing, malware, data privacy, intrusion detection system (IDS), and social engineering How AI automation addresses various security challenges in daily workflows and how to perform automated analyses to proactively mitigate threats Offensive technologies grouped together and analyzed at a higher level from both an offensive and defensive standpoint Providing detailed coverage of a rapidly expanding field, Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection is an essential resource for a wide variety of researchers, scientists, and professionals involved in fields that intersect with cybersecurity, artificial intelligence, and machine learning.

Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock

Leverage the power of Artificial Intelligence (AI) to drive the growth and success of your organization. This book thoroughly explores the reasons why it is so hard to implement AI, and highlights the need to reconcile the motivations and goals of two very different groups of people, business-minded and technical-minded. Divided into four main parts (First Principles, The Why, The What, The How), you'll review case studies and examples from companies that have successfully implemented AI. Part 1 provides a comprehensive overview of the First Principles approach and its basic conventions. Part 2 provides an in-depth look at the current state of AI and why it is increasingly important to businesses of all sizes. Part 3 delves into the key concepts and technologies of AI. Part 4 shares practical guidance and actionable steps for businesses looking to implement AI. Grow Your Business with AI is a must-read for anyone looking to understand and harness the power of AI for business growth and to stay ahead of the curve. What You'll Learn Review the key concepts and technologies of AI, including machine learning, natural language processing, and computer vision Apply the benefits of AI, including increased efficiency, improved decision-making, and new revenue streams in different industries Integrate AI into existing systems and processes. Who This Book Is For Entrepreneurs, business leaders, and professionals looking to leverage the power of AI to drive growth and success for their organizations.

A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples. Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes: Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learning Expansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learning Practical AI and Python “cheat sheet” quick references This hands-on AI programming guide is perfect for anyone with a basic knowledge of programming—including familiarity with variables, arrays, loops, if-else statements, and file input and output—who seeks to understand foundational concepts in AI and AI development.

Make AI technology the backbone of your organization to compete in the Fintech era The rise of artificial intelligence is nothing short of a technological revolution. AI is poised to completely transform asset management and investment banking, yet its current application within the financial sector is limited and fragmented. Existing AI implementations tend to solve very narrow business issues, rather than serving as a powerful tech framework for next-generation finance. Artificial Intelligence for Asset Management and Investment provides a strategic viewpoint on how AI can be comprehensively integrated within investment finance, leading to evolved performance in compliance, management, customer service, and beyond. No other book on the market takes such a wide-ranging approach to using AI in asset management. With this guide, you’ll be able to build an asset management firm from the ground up—or revolutionize your existing firm—using artificial intelligence as the cornerstone and foundation. This is a must, because AI is quickly growing to be the single competitive factor for financial firms. With better AI comes better results. If you aren’t integrating AI in the strategic DNA of your firm, you’re at risk of being left behind. See how artificial intelligence can form the cornerstone of an integrated, strategic asset management framework Learn how to build AI into your organization to remain competitive in the world of Fintech Go beyond siloed AI implementations to reap even greater benefits Understand and overcome the governance and leadership challenges inherent in AI strategy Until now, it has been prohibitively difficult to map the high-tech world of AI onto complex and ever-changing financial markets. Artificial Intelligence for Asset Management and Investment makes this difficulty a thing of the past, providing you with a professional and accessible framework for setting up and running artificial intelligence in your financial operations.

Millions of non-technical professionals and leaders want to understand Artificial Intelligence (AI) and Machine Learning (ML) — whether to improve their businesses, be more effective citizens, consumers or policymakers, or just out of sheer curiosity. Until now, most books on the subject have either been too complicated and mathematical, or have simply avoided the big picture by focusing on the use of specific software libraries. In , Doug Rose bridges the gap, offering today’s most accessible and useful introduction to AI and ML technologies — and what they can and can’t do. Artificial Intelligence for Business Rose begins by tracing AI’s evolution from the early 1950s to the present, illuminating core ideas that still drive its development. Next, he explores recent innovations that have reinvigorated the field by providing the “big data” that makes machine learning so powerful – innovations such as GPS, social media and electronic transactions. Finally, he explains how today’s machines learn by combining powerful processing, advanced algorithms, and artificial neural networks that mimic the human brain. Throughout, he illustrates key concepts with practical examples that help you connect AI, ML, and neural networks to specific problems and solutions. Step by step, he systematically demystifies these powerful technologies, removing the fear, bewilderment, and advanced math — so you can understand the new possibilities they create, and start using them.

The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about

Artificial Intelligence Business: How you can profit from AI is your essential guide to understanding how AI shapes the modern business landscape. This book guides you through the power of machine learning and artificial intelligence, revealing how these technologies can elevate businesses and impact society. What this Book will help me do Gain insight into how artificial intelligence can foster innovative cultures in enterprises. Learn key strategies for utilizing AI to accelerate start-up success. Understand the application of AI in fields such as manufacturing, logistics, and content generation. Discover how text and image generation technologies are transforming modern industries. Explore the societal and political implications of artificial intelligence. Author(s) Noelle Silver Russel and Przemek Chojecki bring extensive expertise in artificial intelligence and business strategy. Noelle is a renowned AI thought leader, with substantial experience in applying machine learning in corporate settings. Przemek is a successful entrepreneur and technologist passionate about innovation in AI. Together, they provide approachable yet informed insights tailored for practical learning. Who is it for? This book is perfect for professionals or enthusiasts with an interest in artificial intelligence and its applications in business. It's suitable for individuals in business roles seeking to enhance their understanding of AI's potential to transform enterprises. The content is designed for learners ranging from AI beginners to those with moderate knowledge looking to explore AI's practical uses. If you're keen on leveraging AI for strategic advantage, this book is for you.

Artificial Intelligence for Business: A Roadmap for Getting Started with AI will provide the reader with an easy to understand roadmap for how to take an organization through the adoption of AI technology. It will first help with the identification of which business problems and opportunities are right for AI and how to prioritize them to maximize the likelihood of success. Specific methodologies are introduced to help with finding critical training data within an organization and how to fill data gaps if they exist. With data in hand, a scoped prototype can be built to limit risk and provide tangible value to the organization as a whole to justify further investment. Finally, a production level AI system can be developed with best practices to ensure quality with not only the application code, but also the AI models. Finally, with this particular AI adoption journey at an end, the authors will show that there is additional value to be gained by iterating on this AI adoption lifecycle and improving other parts of the organization.

"Hands-On Artificial Intelligence for Beginners" is your gateway to understanding and implementing modern AI technologies. This book introduces foundational AI concepts, delves into machine learning, deep learning, and neural networks, and guides you through practical applications in real-world scenarios. What this Book will help me do Understand and apply core AI and machine learning principles using tools like TensorFlow. Develop and train artificial neural networks for various applications. Implement advanced models like CNNs, RNNs, and generative models to solve real-world tasks. Explore reinforcement learning techniques and their game-playing strategies. Design, deploy, and optimize scalable AI systems for long-term use. Author(s) None Dindi and Patrick D. Smith are experts in Artificial Intelligence with extensive teaching and development experience. They dedicate their writing to demystifying complex ideas and making them accessible to learners. Their commitment to hands-on practice ensures that readers build concrete skills while grasping theoretical concepts. Who is it for? If you're an aspiring data scientist or developer keen to break into Artificial Intelligence, this book is perfect for you. Beginners with basic programming knowledge will feel comfortable progressing through the material. Readers looking for practical illustrations of AI concepts will benefit greatly from the hands-on approach. This book is tailored for learners aiming to build and deploy real-world AI systems efficiently.

Technological breakthroughs relating to artificial intelligence has redefined business operations worldwide. For example, the ways in which data is captured, processed, and utilized to optimize customer interactions has grown by leaps and bounds. The change is redefining the structural dynamics of business strategy, economic theory, and management concepts. Leading technology companies around the world have expanded their research in artificial intelligence. With IBM’s launch of Watson, a new cognitive era has started. Investment firms have backed numerous emerging artificial intelligence companies. Meanwhile, there is paucity of academic and business research on the subject. This book project is a pioneering examination of how artificial intelligence is transforming the contemporary business strategy.