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| Author | : Vincent Granville |
| Publisher | : Elsevier |
| Total Pages | : 410 |
| Release | : 2024-01-25 |
| Genre | : Computers |
| ISBN | : 0443218560 |
Download Synthetic Data and Generative AI Book in PDF, ePub and Kindle
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
| Author | : Anand Vemula |
| Publisher | : Independently Published |
| Total Pages | : 0 |
| Release | : 2024-06-04 |
| Genre | : Computers |
| ISBN | : |
Download Synthetic Data and Generative AI Book in PDF, ePub and Kindle
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.
| Author | : Chad Hendren |
| Publisher | : Independently Published |
| Total Pages | : 0 |
| Release | : 2024-07-03 |
| Genre | : Business & Economics |
| ISBN | : |
Download Generative AI Book in PDF, ePub and Kindle
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.
| Author | : Sergey I. Nikolenko |
| Publisher | : Springer Nature |
| Total Pages | : 348 |
| Release | : 2021-06-26 |
| Genre | : Computers |
| ISBN | : 3030751783 |
Download Synthetic Data for Deep Learning Book in PDF, ePub and Kindle
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.
| Author | : Khaled El Emam |
| Publisher | : O'Reilly Media |
| Total Pages | : 166 |
| Release | : 2020-05-19 |
| Genre | : Computers |
| ISBN | : 1492072710 |
Download Practical Synthetic Data Generation Book in PDF, ePub and Kindle
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
| Author | : James Urquhart |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 280 |
| Release | : 2021-01-06 |
| Genre | : Computers |
| ISBN | : 1492075841 |
Download Flow Architectures Book in PDF, ePub and Kindle
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
| Author | : Laurence Moroney |
| Publisher | : O'Reilly Media |
| Total Pages | : 393 |
| Release | : 2020-10-01 |
| Genre | : Computers |
| ISBN | : 1492078166 |
Download AI and Machine Learning for Coders Book in PDF, ePub and Kindle
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
| Author | : Anand Vemula |
| Publisher | : Anand Vemula |
| Total Pages | : 25 |
| Release | : |
| Genre | : Computers |
| ISBN | : |
Download Generative AI for Data Privacy: Unlocking Innovation, Protecting Rights Book in PDF, ePub and Kindle
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.
| Author | : Gomathi Sankar, Jeganathan |
| Publisher | : IGI Global |
| Total Pages | : 468 |
| Release | : 2024-08-21 |
| Genre | : Computers |
| ISBN | : |
Download Generative AI and Implications for Ethics, Security, and Data Management Book in PDF, ePub and Kindle
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.
| Author | : Martin Musiol |
| Publisher | : John Wiley & Sons |
| Total Pages | : 315 |
| Release | : 2023-01-08 |
| Genre | : Business & Economics |
| ISBN | : 1394205945 |
Download Generative AI Book in PDF, ePub and Kindle
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.
