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
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 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 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.
Practicing Trustworthy Machine Learning
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Author : Yada Pruksachatkun
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-01-03
Practicing Trustworthy Machine Learning written by Yada Pruksachatkun 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 2023-01-03 with Business & Economics categories.
With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn: Methods to explain ML models and their outputs to stakeholders How to recognize and fix fairness concerns and privacy leaks in an ML pipeline How to develop ML systems that are robust and secure against malicious attacks Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention
Machine Learning For Financial Risk Management With Python
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Author : Abdullah Karasan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-12-07
Machine Learning For Financial Risk Management With Python written by Abdullah Karasan 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 2021-12-07 with Computers categories.
Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models
Intelligent Data Engineering And Automated Learning Ideal 2020
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Author : Cesar Analide
language : en
Publisher: Springer Nature
Release Date : 2020-10-29
Intelligent Data Engineering And Automated Learning Ideal 2020 written by Cesar Analide and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-29 with Computers categories.
This two-volume set of LNCS 12489 and 12490 constitutes the thoroughly refereed conference proceedings of the 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020, held in Guimaraes, Portugal, in November 2020.* The 93 papers presented were carefully reviewed and selected from 134 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2020 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspiredmodels, agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI. * The conference was held virtually due to the COVID-19 pandemic.
Trustworthy Multimodal Intelligent Systems For Independent Living
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Author : Md Zia Uddin
language : en
Publisher: Springer Nature
Release Date : 2025-08-22
Trustworthy Multimodal Intelligent Systems For Independent Living written by Md Zia Uddin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-22 with Computers categories.
This book is an essential guide for anyone interested in how artificial intelligence can enhance the quality of life for individuals who wish to maintain autonomy in their own homes. The author begins by introducing the reader to AI applications in independent living environments, such as smart assisted homes and AI-driven personalization, and thoughtfully explores the ethical challenges involved. With a strong focus on the intersection of technology and human needs, the book provides a detailed roadmap for building intelligent systems that promote safety, independence, and dignity, especially for elderly or vulnerable populations. The author offers both foundational knowledge and critical discussions around the opportunities and limitations of AI when applied to daily life scenarios. A major strength of the book lies in its thorough examination of multimodal systems. Readers are introduced to a rich array of sensor technologies including wearable devices, environmental sensors, vision-based systems, and sound-based inputs. These components are described not only in terms of their individual functionalities but also in how they interact and fuse data to support complex inference tasks. The text walks the reader through system architectures—centralized and distributed—while emphasizing data fusion, synchronization, and real-time versus batch processing. Through practical examples such as fall detection alerts and activity recognition, the book highlights the engineering challenges and solutions involved in building robust, responsive, and user-accepted assistive systems. Ethical deployment, user engagement, long-term maintenance, and family involvement are all addressed in ways that reflect real-world concerns and user diversity. The book also tackles some of the most pressing topics in AI today: data privacy, explainability, and trust. With an entire section dedicated to synthetic data, it explains how artificial data can be used to train effective models while safeguarding user privacy.
Trust And Privacy In Digital Business
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Author : Sokratis Katsikas
language : en
Publisher: Springer
Release Date : 2004-11-02
Trust And Privacy In Digital Business written by Sokratis Katsikas and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-11-02 with Business & Economics categories.
Sincerely welcome to proceedings of the 1st International Conference on Trust and Privacy in Digital Business, Zaragoza, Spain, held from August 30th to September 1st, 2004. This conference was an outgrowth of the two successful TrustBus inter- tional workshops, held in 2002 and 2003 in conjunction with the DEXA conferences in Aix-en-Provence and in Prague. Being the first of a planned series of successful conferences it was our goal that this event would initiate a forum to bring together researchers from academia and commercial developers from industry to discuss the state of the art of technology for establishing trust and privacy in digital business. We thank you all the attendees for coming to Zaragoza to participate and debate the new emerging advances in this area. The conference program consisted of one invited talk and nine regular technical papers sessions. The invited talk and keynote speech was delivered by Ahmed Patel from the Computer Networks and Distributed Systems Research Group, University College Dublin, Ireland on “Developing Secure, Trusted and Auditable Services for E-Business: An Autonomic Computing Approach”. A paper covering his talk is also contained in this book. The regular paper sessions covered a broad range of topics, from access control - sues to electronic voting, from trust and protocols to digital rights management. The conference attracted close to 100 submissions of which the program committee - cepted 29 papers for presentation and inclusion in the conference proceedings.