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Author: Khaled El Emam
Publisher: "O'Reilly Media, Inc."
ISBN: 1492072699
Category : Computers
Languages : en
Pages : 170
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Book Description
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: Khaled El Emam
Publisher: "O'Reilly Media, Inc."
ISBN: 1492072699
Category : Computers
Languages : en
Pages : 170
Get Book Here
Book Description
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: Sergey I. Nikolenko
Publisher: Springer Nature
ISBN: 3030751783
Category : Computers
Languages : en
Pages : 348
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Book Description
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: Paris Buttfield-Addison
Publisher: "O'Reilly Media, Inc."
ISBN: 1492089893
Category : Computers
Languages : en
Pages : 334
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Book Description
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
Author: Jörg Drechsler
Publisher: Springer Science & Business Media
ISBN: 146140326X
Category : Social Science
Languages : en
Pages : 148
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Book Description
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.
Author: Shin Tsuge
Publisher: Elsevier
ISBN: 0444538933
Category : Science
Languages : en
Pages : 405
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Book Description
In this data book, both conventional Py-GC/MS where thermal energy alone is used to cause fragmentation of given polymeric materials and reactive Py-GC/MS in the presence of organic alkaline for condensation polymers are compiled. Before going into detailed presentation of the data, however, acquiring a firm grip on the proper understanding about the situation of Py-GC/MS would promote better utilization of the following pyrolysis data for various polymers samples. This book incorporates recent technological advances in analytical pyrolysis methods especially useful for the characterization of 163 typical synthetic polymers. The book briefly reviews the instrumentation available in advanced analytical pyrolysis, and offers guidance to perform effectually this technique combining with gas chromatography and mass spectrometry. Main contents are comprehensive sample pyrograms, thermograms, identification tables, and representative mass spectra (MS) of pyrolyzates for synthetic polymers. This edition also highlights thermally-assisted hydrolysis and methylation technique effectively applied to 33 basic condensation polymers. - Coverage of Py-GC/MS data of conventional pyrograms and thermograms of basic 163 kinds of synthetic polymers together with MS and retention index data for pyrolyzates, enabling a quick identification - Additional coverage of the pyrograms and their related data for 33 basic condensation polymers obtained by the thermally-assisted hydrolysis and methylation technique - All compiled data measured under the same experimental conditions for pyrolysis, gas chromatography and mass spectrometry to facilitate peak identification - Surveyable instant information on two facing pages dedicated to the whole data of a given polymer sample
Author: Abdulrahman Kerim
Publisher: Packt Publishing Ltd
ISBN: 1803232609
Category : Computers
Languages : en
Pages : 209
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Book Description
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.
Author: Robert Johnson
Publisher: HiTeX Press
ISBN:
Category : Computers
Languages : en
Pages : 214
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Book Description
"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.
Author: Hideyuki Matsumi
Publisher: Bloomsbury Publishing
ISBN: 1509975993
Category : Law
Languages : en
Pages : 317
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Book Description
This book explores the complexity and depths of our digital world by providing a selection of analyses and discussions from the 16th annual international conference on Computers, Privacy and Data Protection (CPDP): Ideas that Drive Our Digital World. The first half of the book focuses on issues related to the GDPR and data. These chapters provide a critical analysis of the 5-year history of the complex GDPR enforcement system, covering: codes of conduct as a potential co-regulation instrument for the market; an interdisciplinary approach to privacy assessment on synthetic data; the ethical implications of secondary use of publicly available personal data; and automating technologies and GDPR compliance. The second half of the book shifts focus to novel issues and ideas that drive our digital world. The chapters offer analyses on social and environmental sustainability of smart cities; reconstructing states as information platforms; stakeholder identification using the example of video-based Active and Assisted Living (AAL); and a human-centred approach to dark patterns. This interdisciplinary book takes readers on an intellectual journey into a wide range of issues and cutting-edge ideas to tackle our ever-evolving digital landscape.
Author: Tshilidzi Marwala
Publisher: Springer Nature
ISBN: 9819792517
Category :
Languages : en
Pages : 251
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Book Description
Author: A.J. Tallón-Ballesteros
Publisher: IOS Press
ISBN: 1643683470
Category : Computers
Languages : en
Pages : 440
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Book Description
Fuzzy logic is vital to applications in the electrical, industrial, chemical and engineering realms, as well as in areas of management and environmental issues. Data mining is indispensible in dealing with big data, massive data, and scalable, parallel and distributed algorithms. This book presents papers from FSDM 2022, the 8th International Conference on Fuzzy Systems and Data Mining. The conference, originally scheduled to take place in Xiamen, China, was held fully online from 4 to 7 November 2022, due to ongoing restrictions connected with the COVID-19 pandemic. This year, FSDM received 196 submissions, of which 47 papers were ultimately selected for presentation and publication after a thorough review process, taking into account novelty, and the breadth and depth of research themes falling under the scope of FSDM. This resulted in an acceptance rate of 23.97%. Topics covered include fuzzy theory, algorithms and systems, fuzzy applications, data mining and the interdisciplinary field of fuzzy logic and data mining. Offering an overview of current research and developments in fuzzy logic and data mining, the book will be of interest to all those working in the field of data science.
