Download or read book Synthetic Data and Generative AI written by Vincent Granville and published by Elsevier. This book was released on 2024-01-25 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Synthetic Data and Generative AI covers the foundations of machine learning, with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including logistic and Lasso – are presented as a single method, without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap, without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods. Emphasizes numerical stability and performance of algorithms (computational complexity) Focuses on explainable AI/interpretable machine learning, with heavy use of synthetic data and generative models, a new trend in the field Includes new, easier construction of confidence regions, without statistics, a simple alternative to the powerful, well-known XGBoost technique Covers automation of data cleaning, favoring easier solutions when possible Includes chapters dedicated fully to synthetic data applications: fractal-like terrain generation with the diamond-square algorithm, and synthetic star clusters evolving over time and bound by gravity

Download or read book Synthetic Data and Generative AI written by Anand Vemula and published by Independently Published. This book was released on 2024-06-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the ever-evolving world of Artificial Intelligence (AI), data is king. But real-world data often comes with limitations: scarcity, privacy concerns, and inherent biases. This is where synthetic data steps in. Synthetic Data and Generative AI: A Developer's Handbook empowers you to harness the power of synthetic data creation using generative AI models. This comprehensive guide equips you with the knowledge and tools to develop and leverage synthetic data for your AI projects. Part 1: Introduction Grasp the challenges of real-world data and discover how synthetic data addresses them. Understand the fundamental concepts of generative AI and its role in creating realistic synthetic data. Part 2: Unveiling the Power of Synthetic Data Explore the numerous benefits of synthetic data, including overcoming data scarcity, mitigating bias, and ensuring data privacy. Witness the vast potential of synthetic data across various industries, from self-driving cars and healthcare to finance and risk management. Part 3: Generative AI Techniques Demystified Dive deep into the two pillars of generative AI: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Learn how these models work, their strengths and weaknesses, and how to choose the right technique for your specific needs. Part 4: Building and Training Generative Models for Developers Gain practical knowledge on pre-processing data and selecting appropriate generative models for your project. Follow step-by-step tutorials (with code examples linked to online resources) to train your own generative models and generate synthetic data tailored to your requirements. Part 5: The Future Landscape Explore cutting-edge advancements in Explainable AI (XAI) for synthetic data generation, ensuring transparency and trust in your models. Learn how to integrate synthetic data generation into your machine learning pipelines for a seamless and efficient AI development workflow. Part 6: Responsible Development and Conclusion Uncover the ethical considerations surrounding synthetic data, including potential biases and the importance of fairness. Gain insights into best practices for developing trustworthy and responsible AI systems using synthetic data. Synthetic Data and Generative AI: A Developer's Handbook is your one-stop guide to mastering this transformative technology. With its clear explanations, practical tutorials, and exploration of future trends, this book empowers you to unlock the full potential of AI in your projects.

Download or read book Synthetic Data for Deep Learning written by Sergey I. Nikolenko and published by Springer Nature. This book was released on 2021-06-26 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field. In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.

Download or read book Generative AI written by Chad Hendren and published by Independently Published. This book was released on 2024-07-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the rapidly evolving landscape of customer experience (CX), businesses are constantly seeking innovative ways to understand, predict, and enhance customer interactions. This groundbreaking book, "Synthetic Data Revolution in Customer Experience: Powering the Future with Generative AI," offers a comprehensive guide to harnessing the power of synthetic data and generative AI in CX applications. From proving CX hypotheses and conducting risk-free experiments to training robust language models, this book demonstrates why synthetic data is not just an option but a necessity in today's data-driven world. It explores how Canary and telemetry testing with synthetic data can provide invaluable insights without compromising real customer data, and delves into the critical role of synthetic data in creating and refining Large Language Models. Written for CX professionals, data scientists, and business leaders alike, this book provides practical strategies for leveraging generative AI to create large, powerful datasets. It offers step-by-step guidance on applying these datasets in customer experience application development and ongoing tests in production environments. Readers will learn: Why synthetic data is crucial for proving CX application hypotheses How to use Canary and telemetry testing with synthetic data for risk-free experimentation The importance of synthetic data in training Large Language Models Practical applications of generative AI in creating robust CX datasets Strategies for implementing synthetic data in CX application development and testing This book is an essential resource for anyone looking to stay ahead in the competitive landscape of customer experience. By embracing the synthetic data revolution, businesses can unlock new levels of innovation, efficiency, and customer satisfaction.

Download or read book Practical Synthetic Data Generation written by Khaled El Emam and published by "O'Reilly Media, Inc.". This book was released on 2020-05-19 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue. Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes: Steps for generating synthetic data using multivariate normal distributions Methods for distribution fitting covering different goodness-of-fit metrics How to replicate the simple structure of original data An approach for modeling data structure to consider complex relationships Multiple approaches and metrics you can use to assess data utility How analysis performed on real data can be replicated with synthetic data Privacy implications of synthetic data and methods to assess identity disclosure

Download or read book Practical Simulations for Machine Learning written by Paris Buttfield-Addison and published by "O'Reilly Media, Inc.". This book was released on 2022-06-07 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learning models.That’s just the beginning. With this practical book, you’ll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. You'll learn how to: Design an approach for solving ML and AI problems using simulations with the Unity engine Use a game engine to synthesize images for use as training data Create simulation environments designed for training deep reinforcement learning and imitation learning models Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization Train a variety of ML models using different approaches Enable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits

Download or read book Practical Synthetic Data Generation written by Khaled El Emam and published by O'Reilly Media. This book was released on 2020-05-19 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue. Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes: Steps for generating synthetic data using multivariate normal distributions Methods for distribution fitting covering different goodness-of-fit metrics How to replicate the simple structure of original data An approach for modeling data structure to consider complex relationships Multiple approaches and metrics you can use to assess data utility How analysis performed on real data can be replicated with synthetic data Privacy implications of synthetic data and methods to assess identity disclosure

Download or read book Flow Architectures written by James Urquhart and published by "O'Reilly Media, Inc.". This book was released on 2021-01-06 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Software development today is embracing events and streaming data, which optimizes not only how technology interacts but also how businesses integrate with one another to meet customer needs. This phenomenon, called flow, consists of patterns and standards that determine which activity and related data is communicated between parties over the internet. This book explores critical implications of that evolution: What happens when events and data streams help you discover new activity sources to enhance existing businesses or drive new markets? What technologies and architectural patterns can position your company for opportunities enabled by flow? James Urquhart, global field CTO at VMware, guides enterprise architects, software developers, and product managers through the process. Learn the benefits of flow dynamics when businesses, governments, and other institutions integrate via events and data streams Understand the value chain for flow integration through Wardley mapping visualization and promise theory modeling Walk through basic concepts behind today's event-driven systems marketplace Learn how today's integration patterns will influence the real-time events flow in the future Explore why companies should architect and build software today to take advantage of flow in coming years

Download or read book AI and Machine Learning for Coders written by Laurence Moroney and published by O'Reilly Media. This book was released on 2020-10-01 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving

Download or read book Generative AI and Implications for Ethics Security and Data Management written by Gomathi Sankar, Jeganathan and published by IGI Global. This book was released on 2024-08-21 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: As generative AI rapidly advances with the field of artificial intelligence, its presence poses significant ethical, security, and data management challenges. While this technology encourages innovation across various industries, ethical concerns regarding the potential misuse of AI-generated content for misinformation or manipulation may arise. The risks of AI-generated deepfakes and cyberattacks demand more research into effective security tactics. The supervision of datasets required to train generative AI models raises questions about privacy, consent, and responsible data management. As generative AI evolves, further research into the complex issues regarding its potential is required to safeguard ethical values and security of people’s data. Generative AI and Implications for Ethics, Security, and Data Management explores the implications of generative AI across various industries who may use the tool for improved organizational development. The security and data management benefits of generative AI are outlined, while examining the topic within the lens of ethical and social impacts. This book covers topics such as cybersecurity, digital technology, and cloud storage, and is a useful resource for computer engineers, IT professionals, technicians, sociologists, healthcare workers, researchers, scientists, and academicians.

Download or read book Generative AI for Data Privacy Unlocking Innovation Protecting Rights written by Anand Vemula and published by Anand Vemula. This book was released on with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: The exciting world of generative AI offers immense potential for innovation, but its reliance on vast amounts of data raises critical data privacy concerns. This book explores this dynamic landscape, equipping you to understand both the power and the potential pitfalls of generative AI. Part 1 dives into the core concepts of generative models, from GANs and VAEs to their diverse capabilities. It then explores the data privacy landscape, highlighting the importance of regulations like GDPR and CCPA in the age of AI. You'll gain insights into the specific challenges generative AI poses to data privacy, such as the risk of data leakage through seemingly anonymized training data. Part 2 delves deeper into these privacy risks. You'll learn how generative models can unintentionally reveal information from their training data and discover techniques to identify and mitigate these leakage risks. The book also explores the potential of synthetic data – artificially generated data that resembles real data but protects privacy. You'll understand the advantages and limitations of synthetic data and explore methods for ensuring privacy-preserving generation techniques. Part 3 focuses on solutions and building trust. It examines cutting-edge privacy-enhancing techniques for generative AI, such as differential privacy and federated learning. These techniques allow training on data while keeping it encrypted or distributed, safeguarding individual privacy. The book also emphasizes the importance of user control and transparency in generative AI development. You'll explore ways to empower users with control over their data and advocate for clear explanations of how generative models function. Part 4 explores the evolving legal and ethical landscape surrounding generative AI. You'll discover potential regulatory approaches for governing its use, emphasizing the need to balance innovation with comprehensive data privacy protection. Finally, the book looks towards the future, exploring the societal and ethical considerations of generative AI. You'll gain insights into potential biases in models and the impact of AI-generated content on creativity. The book concludes with recommendations for responsible development and use of generative AI, ensuring it thrives as a force for good that respects individual privacy. This comprehensive book empowers you to navigate the world of generative AI responsibly. Whether you're a developer, a data privacy professional, or simply curious about this transformative technology, "Generative AI for Data Privacy" provides the knowledge and tools you need to understand its potential and navigate its complexities.

Download or read book Machine Learning for Asset Managers written by Marcos M. López de Prado and published by Cambridge University Press. This book was released on 2020-04-22 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to "learn" complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.

Download or read book Generative AI Business Applications written by David E. Sweenor and published by TinyTechMedia LLC. This book was released on 2024-01-31 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: Within the past year, generative AI has broken barriers and transformed how we think about what computers are truly capable of. But, with the marketing hype and generative AI washing of content, it’s increasingly difficult for business leaders and practitioners to go beyond the art of the possible and answer that critical question–how is generative AI actually being used in organizations? With over 70 real-world case studies and applications across 12 different industries and 11 departments, Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies fills a critical knowledge gap for business leaders and practitioners by providing examples of generative AI in action. Diving into the case studies, this TinyTechGuide discusses AI risks, implementation considerations, generative AI operations, AI ethics, and trustworthy AI. The world is transforming before our very eyes. Don’t get left behind—while understanding the powers and perils of generative AI. Full of use cases and real-world applications, this book is designed for business leaders, tech professionals, and IT teams. We provide practical, jargon-free explanations of generative AI's transformative power. Gain a competitive edge in today's marketplace with Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies. Remember, it's not the tech that's tiny, just the book!™

Download or read book Generative AI written by Martin Musiol and published by John Wiley & Sons. This book was released on 2023-01-08 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: An engaging and essential discussion of generative artificial intelligence In Generative AI: Navigating the Course to the Artificial General Intelligence Future, celebrated author Martin Musiol—founder and CEO of generativeAI.net and GenAI Lead for Europe at Infosys—delivers an incisive and one-of-a-kind discussion of the current capabilities, future potential, and inner workings of generative artificial intelligence. In the book, you'll explore the short but eventful history of generative artificial intelligence, what it's achieved so far, and how it's likely to evolve in the future. You'll also get a peek at how emerging technologies are converging to create exciting new possibilities in the GenAI space. Musiol analyzes complex and foundational topics in generative AI, breaking them down into straightforward and easy-to-understand pieces. You'll also find: Bold predictions about the future emergence of Artificial General Intelligence via the merging of current AI models Fascinating explorations of the ethical implications of AI, its potential downsides, and the possible rewards Insightful commentary on Autonomous AI Agents and how AI assistants will become integral to daily life in professional and private contexts Perfect for anyone interested in the intersection of ethics, technology, business, and society—and for entrepreneurs looking to take advantage of this tech revolution—Generative AI offers an intuitive, comprehensive discussion of this fascinating new technology.

Download or read book Artificial Intelligence in Practice written by Bernard Marr and published by John Wiley & Sons. This book was released on 2019-04-15 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce.

Download or read book Generative AI in Practice written by Bernard Marr and published by John Wiley & Sons. This book was released on 2024-03-25 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the future as we journey through the next frontier of technological advancement Generative AI isn't just the biggest trend right now; it's the pinnacle of today's technological evolution. Beyond the capabilities of ChatGPT and similar AIs that can generate written content and artwork, GenAI is rewriting the rulebook. From crafting intricate industrial designs, writing computer code, and producing mesmerizing synthetic voices to composing enchanting music and innovating genetic breakthroughs, the horizons are limitless. Picture a world where your daily news is read by your favorite celebrity, where video games conjure unparalleled universes in real-time, where machines concoct groundbreaking medicines, and where literature and courses are tailored flawlessly for you. In Generative AI in Practice, renowned futurist Bernard Marr offers readers a deep dive into the captivating universe of GenAI. This comprehensive guide not only introduces the uninitiated to this groundbreaking technology but outlines the profound and unprecedented impact of GenAI on the fabric of business and society. It's set to redefine all our jobs, revolutionize business operations, and question the very foundations of existing business models. Beyond merely altering, GenAI promises to elevate the products and services at the heart of enterprises and intricately weave itself into the tapestry of our daily lives. Through 19 enriching chapters, Marr canvases a vast array of sectors, shedding light on the most innovative real-world GenAI applications through practical examples and how they are molding the contours of various industries including retail, healthcare, education, and finance. Marr discusses the exciting innovations in media and entertainment to the seismic shifts in advertising, customer engagement and beyond, but also critically addresses the risks, challenges, and the future trajectory of GenAI. Throughout the pages of this book, you will: Navigate the complex landscapes of risks and challenges posed by GenAI. Delve into the revolutionary transformation of the job market in the age of GenAI. Discover how retail is evolving with virtual try-ons and AI-powered personalization. Dive deep into the transformative impact on education, offering truly personalized learning experiences. Witness the metamorphosis of healthcare, from AI-aided drug discoveries to custom advice. Explore the boundless potentials in media, design, banking, coding, and even the legal arena. Ideal for professionals, technophiles, and anyone eager to understand the next big thing in technology and its monumental impact on our world, Generative AI In Practice will equip readers with insights on how to implement GenAI, how GenAI is different to traditional AI, and a comprehensive list of generative AI tools in the appendix.

Download or read book Introduction to Generative AI written by Numa Dhamani and published by Simon and Schuster. This book was released on 2024-02-27 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI tools like ChatGPT are amazing—but how will their use impact our society? This book introduces the world-transforming technology and the strategies you need to use generative AI safely and effectively. Introduction to Generative AI gives you the hows-and-whys of generative AI in accessible language. In this easy-to-read introduction, you’ll learn: How large language models (LLMs) work How to integrate generative AI into your personal and professional workflows Balancing innovation and responsibility The social, legal, and policy landscape around generative AI Societal impacts of generative AI Where AI is going Anyone who uses ChatGPT for even a few minutes can tell that it’s truly different from other chatbots or question-and-answer tools. Introduction to Generative AI guides you from that first eye-opening interaction to how these powerful tools can transform your personal and professional life. In it, you’ll get no-nonsense guidance on generative AI fundamentals to help you understand what these models are (and aren’t) capable of, and how you can use them to your greatest advantage. Foreword by Sahar Massachi. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Generative AI tools like ChatGPT, Bing, and Bard have permanently transformed the way we work, learn, and communicate. This delightful book shows you exactly how Generative AI works in plain, jargon-free English, along with the insights you’ll need to use it safely and effectively. About the book Introduction to Generative AI guides you through benefits, risks, and limitations of Generative AI technology. You’ll discover how AI models learn and think, explore best practices for creating text and graphics, and consider the impact of AI on society, the economy, and the law. Along the way, you’ll practice strategies for getting accurate responses and even understand how to handle misuse and security threats. What's inside How large language models work Integrate Generative AI into your daily work Balance innovation and responsibility About the reader For anyone interested in Generative AI. No technical experience required. About the author Numa Dhamani is a natural language processing expert working at the intersection of technology and society. Maggie Engler is an engineer and researcher currently working on safety for large language models. The technical editor on this book was Maris Sekar. Table of Contents 1 Large language models: The power of AI Evolution of natural language processing 2 Training large language models 3 Data privacy and safety with LLMs 4 The evolution of created content 5 Misuse and adversarial attacks 6 Accelerating productivity: Machine-augmented work 7 Making social connections with chatbots 8 What’s next for AI and LLMs 9 Broadening the horizon: Exploratory topics in AI