Practical Synthetic Data Generation
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Practical Synthetic Data Generation
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Author : Khaled El Emam
language : en
Publisher: O'Reilly Media
Release Date : 2020-05-31
Practical Synthetic Data Generation written by Khaled El Emam and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-31 with Computers categories.
One challenge with big data and other secondary analytics initiatives is getting access to large and diverse data. Secondary analytics allow insights beyond the questions that data initially collected can answer. This practical book introduces techniques for generating synthetic data-fake data generated from real data-that can provide secondary analytics to help you understand customer behaviors, develop new products, or generate new revenue. CTOs, CIOs, and directors of analytics 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 of synthetic data generation from real data sets. Business leaders will examine how synthetic data can help accelerate time to a solution.
Practical Synthetic Data Generation
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Author : Khaled El Emam
language : en
Publisher: O'Reilly Media
Release Date : 2020-05-19
Practical Synthetic Data Generation written by Khaled El Emam and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-19 with Computers categories.
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
Synthetic Datasets For Statistical Disclosure Control
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Author : Jörg Drechsler
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-06-24
Synthetic Datasets For Statistical Disclosure Control written by Jörg Drechsler and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-24 with Social Science categories.
The aim of this book is to give the reader a detailed introduction to the different approaches to generating multiply imputed synthetic datasets. It describes all approaches that have been developed so far, provides a brief history of synthetic datasets, and gives useful hints on how to deal with real data problems like nonresponse, skip patterns, or logical constraints. Each chapter is dedicated to one approach, first describing the general concept followed by a detailed application to a real dataset providing useful guidelines on how to implement the theory in practice. The discussed multiple imputation approaches include imputation for nonresponse, generating fully synthetic datasets, generating partially synthetic datasets, generating synthetic datasets when the original data is subject to nonresponse, and a two-stage imputation approach that helps to better address the omnipresent trade-off between analytical validity and the risk of disclosure. The book concludes with a glimpse into the future of synthetic datasets, discussing the potential benefits and possible obstacles of the approach and ways to address the concerns of data users and their understandable discomfort with using data that doesn’t consist only of the originally collected values. The book is intended for researchers and practitioners alike. It helps the researcher to find the state of the art in synthetic data summarized in one book with full reference to all relevant papers on the topic. But it is also useful for the practitioner at the statistical agency who is considering the synthetic data approach for data dissemination in the future and wants to get familiar with the topic.
Practical Simulations For Machine Learning
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Author : Paris Buttfield-Addison
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-06-07
Practical Simulations For Machine Learning written by Paris Buttfield-Addison and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-07 with Computers categories.
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
Digital Professionalism In Health And Care Developing The Workforce Building The Future
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Author : P. Scott
language : en
Publisher: IOS Press
Release Date : 2022-09-29
Digital Professionalism In Health And Care Developing The Workforce Building The Future written by P. Scott and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-29 with Medical categories.
Digital technology has become integral in the fields of health and care, and a number of recent reports have stressed the importance of equipping health and care staff with the skills and knowledge they need to use such technology effectively. Numerous failures of digital projects in the health and care sectors have demonstrated that simply relocating IT generalists into these specialist fields is not a guaranteed formula for success; the unique complexities of the typically under-resourced legacy infrastructures of health and care create challenges that demand specific education and training. This book presents the proceedings of the European Federation for Medical Informatics (EFMI) 2022 Special Topic Conference (STC), held in Cardiff, Wales, on 7-8 September 2022. The theme of STC 2022 was Digital Professionalism in Health and Care: Developing the Workforce, Building the Future, which emphasized the vital need for professional education, training and continuing development of the health and care informatics workforce. The 30 full papers and 5 posters in this book cover a broad range of topics and methods in informatics education and training, and include a small selection from the wider sub-domains of biomedical informatics. Providing a valuable overview of current methods and training, the book will be of interest to a wide range of professionals working in healthcare today, especially those involved in equipping the workforce with the skills they will need for the digital future.
Synthetic Content And Its Implications For Ai Policy
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Author : Sarmiento, Camilo
language : en
Publisher: UNESCO Publishing
Release Date : 2024-12-11
Synthetic Content And Its Implications For Ai Policy written by Sarmiento, Camilo and has been published by UNESCO Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-11 with Political Science categories.
Synthetic Data And Generative Ai
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Author : Vincent Granville
language : en
Publisher: Elsevier
Release Date : 2024-01-09
Synthetic Data And Generative Ai written by Vincent Granville and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-09 with Computers categories.
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
Data Science The Hard Parts
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Author : Daniel Vaughan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-11-01
Data Science The Hard Parts written by Daniel Vaughan and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-01 with Computers categories.
This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline—machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries. With this book, you will: Understand how data science creates value Deliver compelling narratives to sell your data science project Build a business case using unit economics principles Create new features for a ML model using storytelling Learn how to decompose KPIs Perform growth decompositions to find root causes for changes in a metric Daniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).
Personalized Medicine In The Making
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Author : Chiara Beneduce
language : en
Publisher: Springer Nature
Release Date : 2022-03-07
Personalized Medicine In The Making written by Chiara Beneduce and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-07 with Medical categories.
This book offers a multidisciplinary look at the much-debated concept of “personalized medicine”. By combining a humanistic and a scientific approach, the book builds up a multidimensional way to understand the limits and potentialities of a personalized approach in medicine and healthcare. The book reflects on personalized medicine and complex diseases, the relationship between personalized medicine and the new bio-technologies, personalized medicine and personalized nutrition, and on some ethical, political, economic, and social implications of personalized medicine. This volume is of interest to researchers from several disciplines including philosophy, bio-medicine, and the social sciences. Chapter 16, “The Impact of Fantasy” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Proceedings Of The Vii Ibero American Congress Of Smart Cities Icsc Cities 2024 12 14 November San Carlos Costa Rica
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Author : Diego Rossit
language : en
Publisher: Springer Nature
Release Date : 2025-05-31
Proceedings Of The Vii Ibero American Congress Of Smart Cities Icsc Cities 2024 12 14 November San Carlos Costa Rica written by Diego Rossit and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-31 with Technology & Engineering categories.
This book compiles high-quality selected papers from the VII Ibero-American Congress of Smart Cities (ICSC-CITIES 2024), a leading event in the field of smart urban development. Smart cities are a response to the increasingly urgent need to reorient our lives towards sustainability. In an era of rapid urbanization and growing environmental challenges, these cities are designed to optimize resources, reduce environmental impact, and enhance the overall quality of life for their citizens. By leveraging advanced infrastructure, innovative solutions, and cutting-edge technology, smart cities aim to create more efficient, resilient, and livable urban environments. Within this framework, energy plays a pivotal role in enhancing the sustainability and functionality of our cities. The papers explore a wide range of topics, including smart grids, electric systems, energy efficiency, urban mobility, environmental monitoring, and other areas critical to the development of sustainable cities. The insights and research presented in this book contribute to the ongoing dialogue on how cities can better serve their populations while addressing the challenges of climate change, resource management, and technological integration. ICSC-CITIES 2024 takes place on November 12-14, 2024, in the vibrant city of San Carlos, Costa Rica, and is organized by Tecnológico de Costa Rica (TEC). As the eighth edition of the Ibero-American Congress of Smart Cities, this conference continues to be a key platform for academics, professionals, and policymakers to share knowledge, exchange ideas, and collaborate on the future of urban living. Authors invite the academic community and industry experts to engage in discussions and contribute to shaping the energy-related aspects and overall development of the cities of tomorrow.