Synthetic Data Generation
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Synthetic Data Generation
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2024-10-27
Synthetic Data Generation written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-27 with Computers categories.
"Synthetic Data Generation: A Beginner’s Guide" offers an insightful exploration into the emerging field of synthetic data, essential for anyone navigating the complexities of data science, artificial intelligence, and technology innovation. This comprehensive guide demystifies synthetic data, presenting a detailed examination of its core principles, techniques, and prospective applications across diverse industries. Designed with accessibility in mind, it equips beginners and seasoned practitioners alike with the necessary knowledge to leverage synthetic data's potential effectively. Delving into the nuances of data sources, generation techniques, and evaluation metrics, this book serves as a practical roadmap for mastering synthetic data. Readers will gain a robust understanding of the advantages and limitations, ethical considerations, and privacy concerns associated with synthetic data usage. Through real-world examples and industry insights, the guide illuminates the transformative role of synthetic data in enhancing innovation while safeguarding privacy. With an eye on both present applications and future trends, "Synthetic Data Generation: A Beginner’s Guide" prepares readers to engage with the evolving challenges and opportunities in data-centric fields. Whether for academic enrichment, professional development, or as a primer for new data enthusiasts, this book stands as an essential resource in understanding and implementing synthetic data solutions.
Practical Synthetic Data Generation
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Author : Khaled El Emam
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-05-19
Practical Synthetic Data Generation written by Khaled El Emam 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 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 Data
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Author : Julie Molin
language : en
Publisher: Independently Published
Release Date : 2023-02-10
Synthetic Data written by Julie Molin and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-10 with categories.
ATTENTION RESEARCHERS, BUSINESS DEVELOPMENT AND PRODUCT ANALYSTS, RESEARCH CONSULTANTS, ETC! Are you tired of being limited by the availability of real-world data? Are you ready to take your business, research, or project to the next level with synthetic data generation? Are you tired of spending endless hours collecting and cleaning data for your business or research projects? Are you ready to unlock the power of synthetic data? Look no further than Synthetic Data: The Future of Data Generation. Synthetic data is a revolutionary new way of creating data that is not only cost-effective and efficient but also ensures data privacy and security. It involves using machine learning algorithms to generate data that mimics real-world data, making it a valuable tool for a variety of industries, including finance, healthcare, and transportation. But where do you even begin when it comes to synthetic data? That's where this book comes in. Synthetic Data: The Future of Data Generation is your comprehensive guide to understanding and utilizing this cutting-edge technology. Inside, you'll find: An overview of the benefits of synthetic data and why it's quickly becoming the go-to choice for data generation. Detailed explanations of the different types of synthetic data and their applications A guide on how to generate synthetic data using various machine learning techniques Information on how to evaluate the quality of synthetic data Real-world examples of how companies and organizations are already using synthetic data to drive their success And much more! With our expert guidance, you'll be able to harness the power of synthetic data to streamline your business operations, improve your research outcomes, and stay competitive in today's data-driven world. Don't miss out on this game-changing technology - order your copy NOW
Synthetic Data
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Author : R. Nidhya
language : en
Publisher: John Wiley & Sons
Release Date : 2025-09-17
Synthetic Data written by R. Nidhya and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-17 with Computers categories.
Synthetic Data For Machine Learning
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Author : Abdulrahman Kerim
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-10-27
Synthetic Data For Machine Learning written by Abdulrahman Kerim and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-27 with Computers categories.
Conquer data hurdles, supercharge your ML journey, and become a leader in your field with synthetic data generation techniques, best practices, and case studies Key Features Avoid common data issues by identifying and solving them using synthetic data-based solutions Master synthetic data generation approaches to prepare for the future of machine learning Enhance performance, reduce budget, and stand out from competitors using synthetic data Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe machine learning (ML) revolution has made our world unimaginable without its products and services. However, training ML models requires vast datasets, which entails a process plagued by high costs, errors, and privacy concerns associated with collecting and annotating real data. Synthetic data emerges as a promising solution to all these challenges. This book is designed to bridge theory and practice of using synthetic data, offering invaluable support for your ML journey. Synthetic Data for Machine Learning empowers you to tackle real data issues, enhance your ML models' performance, and gain a deep understanding of synthetic data generation. You’ll explore the strengths and weaknesses of various approaches, gaining practical knowledge with hands-on examples of modern methods, including Generative Adversarial Networks (GANs) and diffusion models. Additionally, you’ll uncover the secrets and best practices to harness the full potential of synthetic data. By the end of this book, you’ll have mastered synthetic data and positioned yourself as a market leader, ready for more advanced, cost-effective, and higher-quality data sources, setting you ahead of your peers in the next generation of ML.What you will learn Understand real data problems, limitations, drawbacks, and pitfalls Harness the potential of synthetic data for data-hungry ML models Discover state-of-the-art synthetic data generation approaches and solutions Uncover synthetic data potential by working on diverse case studies Understand synthetic data challenges and emerging research topics Apply synthetic data to your ML projects successfully Who this book is forIf you are a machine learning (ML) practitioner or researcher who wants to overcome data problems, this book is for you. Basic knowledge of ML and Python programming is required. The book is one of the pioneer works on the subject, providing leading-edge support for ML engineers, researchers, companies, and decision makers.
Using Machine Learning Techniques For Prediction And Data Generation With Applications To Data Privacy
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Author : Nazmiye Ceren Abay
language : en
Publisher:
Release Date : 2019
Using Machine Learning Techniques For Prediction And Data Generation With Applications To Data Privacy written by Nazmiye Ceren Abay and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Artificial intelligence categories.
Increasingly, machine learning (ML) applications are developed and become an integral part of many real-world applications. Especially, ML techniques are heavily used in research and industry to help make effective decisions. Despite the apparent recent success of ML techniques, there exist some domain-specific challenges that require in-depth investigations with respect to predictive accuracy, privacy protection and cybersecurity. In this dissertation, we start with understanding the usability of ML techniques in the cryptocurrency transaction domain (e.g., Bitcoin) where there is no privacy concern (i.e., all Bitcoin transaction information is public) and show how to use ML techniques to make better predictions in real-time. For application domains that involve sensitive data, collecting, sharing and refining of these sensitive data may raise serious privacy concerns. To address these concerns, we propose a privacy preserving synthetic data generation technique that leverages deep learning. The proposed technique allows participants to share the synthetic datasets freely without worrying about the individual privacy. Furthermore, we compare our proposed technique with the existing synthetic data generation algorithms, and investigate the utility of these algorithms under different use cases. Finally, we explore the usage of the generated synthetic data to improve the cybersecurity posture of the organizations. Basically, we show that the generated synthetic data not only protect individual privacy but can be used to deceive (i.e., the synthetic data is indistinguishable from the real data) the potential cyberattackers. This in return could be used to reduce sensitive data leakage under successful cyberattacks where an attacker could be deceived to target synthetic data instead of the real, and sensitive data.
Synthetic Data For Deep Learning
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Author : Sergey I. Nikolenko
language : en
Publisher: Springer Nature
Release Date : 2021-06-26
Synthetic Data For Deep Learning written by Sergey I. Nikolenko and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-26 with Computers categories.
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.
Synthetic Data For Deep Learning
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Author : Necmi Gürsakal
language : en
Publisher: Apress
Release Date : 2022-11-16
Synthetic Data For Deep Learning written by Necmi Gürsakal and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-16 with Computers categories.
Data is the indispensable fuel that drives the decision making of everything from governments, to major corporations, to sports teams. Its value is almost beyond measure. But what if that data is either unavailable or problematic to access? That’s where synthetic data comes in. This book will show you how to generate synthetic data and use it to maximum effect. Synthetic Data for Deep Learning begins by tracing the need for and development of synthetic data before delving into the role it plays in machine learning and computer vision. You’ll gain insight into how synthetic data can be used to study the benefits of autonomous driving systems and to make accurate predictions about real-world data. You’ll work through practical examples of synthetic data generation using Python and R, placing its purpose and methods in a real-world context. Generative Adversarial Networks (GANs) are also covered in detail, explaining how they work and their potential applications. After completing this book, you’ll have the knowledge necessary to generate and use synthetic data to enhance your corporate, scientific, or governmental decision making. What You Will Learn Create synthetic tabular data with R and Python Understand how synthetic data is important for artificial neural networks Master the benefits and challenges of synthetic data Understand concepts such as domain randomization and domain adaptation related to synthetic data generation Who This Book Is For Those who want to learn about synthetic data and its applications, especially professionals working in the field of machine learning and computer vision. This book will also be useful for graduate and doctoral students interested in this subject.
Sdv
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Author : Andrew Montanez (M. Eng.)
language : en
Publisher:
Release Date : 2018
Sdv written by Andrew Montanez (M. Eng.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
In this thesis, I designed three open source Python libraries with the intention of creating a robust system that can accurately generate synthetic data. The goals of this thesis were to separate the different components in synthetic data generation into their own libraries. We identified these components as consisting of a way to transform the data, a way to model the data, and a way to recursively traverse the data set to model the relationships between the table as well as the data set itself. Once the libraries were implemented and functioning, we designed a program to run the synthetic data generation process in parallel on subsets of the original data. The goal of this program was to see if the overall modeling time could be reduced by modeling subsets in parallel and then averaging the parameters. In the end, we test how close these averaged parameters are to the original to see if this is a valid modeling technique.
Accelerating Ai With Synthetic Data
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Author : Khaled Emam
language : en
Publisher:
Release Date : 2020
Accelerating Ai With Synthetic Data written by Khaled Emam and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Recently, data scientists have found effective methods to generate high-quality synthetic data. That's good news for companies seeking large amounts of data to train and build artificial intelligence and machine learning models. This report provides an overview of synthetic data generation that not only focuses on business value and use cases but also provides some practical techniques for using synthetic data. Author Khaled El Emam, cofounder and Director of Replica Analytics and Professor at the University of Ottawa, helps data analytics leadership understand the options so they can get started building their own training sets. With the help of several industry use cases, you'll learn how synthetic data can accelerate machine learning projects in your company. As advances in synthetic data generation continue, broad adoption of this approach will quickly follow. Learn what synthetic data is and how it can accelerate machine learning model development Understand how synthetic data is generated-and why these datasets are similar to real data Explore the process and best practices for generating synthetic datasets Examine case studies of synthetic data use in industries including manufacturing, healthcare, financial services, and transportation Learn key requirements for future work and improvements to synthetic data.