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Synthetic Data


Synthetic Data
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Synthetic Data For Deep Learning


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 Generation


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.



Synthetic Data For Machine Learning


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.



Practical Synthetic Data Generation


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



Practical Synthetic Data Generation


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



Advances In Information And Communication


Advances In Information And Communication
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Author : Kohei Arai
language : en
Publisher: Springer Nature
Release Date : 2025-03-04

Advances In Information And Communication written by Kohei Arai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-04 with Computers categories.


This book comprises the proceedings of the Future of Information and Communication Conference (FICC) 2025, held on 28-29 April 2025 in Berlin, Germany. The conference brought together leading researchers, industry experts, and academics from across the globe to discuss the latest advancements, challenges, and opportunities in the rapidly evolving field of information and communication technologies. The conference received an impressive 401 submissions, of which 138 high-quality papers were selected after a rigorous peer-review process. These contributions span a diverse range of topics, including artificial intelligence, cybersecurity, data science, networking, human–computer interaction, and more. FICC 2025 provided an engaging platform for collaboration and knowledge exchange, highlighting state-of-the-art research and practical solutions to global challenges. This proceedings book serves as a valuable resource for researchers, practitioners, and innovators seeking insights into the future of information and communication technologies.



Mechanism Design Behavioral Science And Artificial Intelligence In International Relations


Mechanism Design Behavioral Science And Artificial Intelligence In International Relations
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Author : Tshilidzi Marwala
language : en
Publisher: Elsevier
Release Date : 2024-07-23

Mechanism Design Behavioral Science And Artificial Intelligence In International Relations written by Tshilidzi Marwala and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-23 with Mathematics categories.


Recent advances in AI and Mechanism Design provide a vital tool for solving collective action problems, common in international relations. By using AI to optimize mechanisms for cooperation and coordination, we can better address issues such as climate change, trade, and security. Mechanism Design, Behavioral Science and Artificial Intelligence in International Relations shows readers how the intersection of Mechanism Design and Artificial Intelligence is revolutionizing the way we approach international relations. By using AI to optimize mechanisms, we can design better institutions, policies, and agreements that are more effective and efficient. Dr. Tshilidzi Marwala, United Nations University Rector and UN Under-Secretary General, presents the essential technologies used in Game Theory, Mechanism Design and AI and applies these to significant global issues such as interstate conflict, cybersecurity, and energy. International relations are a complex field, with many different actors and interests in play. By incorporating AI into our analysis and decision-making processes, we can better understand and predict the behavior of multiple actors and design mechanisms that take these behaviors into account, thereby producing more desirable and creative interdisciplinary approaches. The book presents real-world applications of these rapidly evolving technologies in crucial research fields such as Interstate Conflict, International Trade, Climate Change, Water management, Energy, cybersecurity, and global finance. - Provides insights for computer scientists, researchers, practitioners, and policymakers on how to develop practical tools to solve many complex problems in international relations, such as climate change, cybersecurity, and interstate conflict - Presents the necessary computer science, mathematical methods, and techniques in AI, game theory, mechanism design, and algorithm development - Includes real-world applications of AI and mechanism design in a wide variety of research topics, such as international conflict, international trade, climate change, water management, energy management, cybersecurity, and global finance



200 Tips For Mastering Generative Ai


200 Tips For Mastering Generative Ai
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Author : Rick Spair
language : en
Publisher: Rick Spair
Release Date :

200 Tips For Mastering Generative Ai written by Rick Spair and has been published by Rick Spair this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.


In the rapidly evolving landscape of artificial intelligence, Generative AI stands out as a transformative force with the potential to revolutionize industries and reshape our understanding of creativity and automation. From its inception, Generative AI has captured the imagination of researchers, developers, and entrepreneurs, offering unprecedented capabilities in generating new data, simulating complex systems, and solving intricate problems that were once considered beyond the reach of machines. This book, "200 Tips for Mastering Generative AI," is a comprehensive guide designed to empower you with the knowledge and practical insights needed to harness the full potential of Generative AI. Whether you are a seasoned AI practitioner, a curious researcher, a forward-thinking entrepreneur, or a passionate enthusiast, this book provides valuable tips and strategies to navigate the vast and intricate world of Generative AI. We invite you to explore, experiment, and innovate with the knowledge you gain from this book. Together, we can unlock the full potential of Generative AI and shape a future where intelligent machines and human creativity coexist and collaborate in unprecedented ways. Welcome to "200 Tips for Mastering Generative AI." Your journey into the fascinating world of Generative AI begins here.



Generative Artificial Intelligence In Finance


Generative Artificial Intelligence In Finance
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Author : Pethuru Raj Chelliah
language : en
Publisher: John Wiley & Sons
Release Date : 2025-01-22

Generative Artificial Intelligence In Finance written by Pethuru Raj Chelliah 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 2025-01-22 with Computers categories.


This comprehensive volume delves deep into the diverse applications and implications of generative AI across accounting, finance, economics, business, and management, providing readers with a holistic understanding of this rapidly evolving landscape. Generative Artificial Intelligence in Finance: Large Language Models, Interfaces, and Industry Use Cases to Transform Accounting and Finance Processes provides a comprehensive guide to ethically harnessing generative AI systems to reshape financial management. Generative AI is a key theme across the accounting and finance sectors to drive significant optimizations leading to sustainability. Across 22 chapters, leading researchers supply innovative applications of large language models across the economic realm. Through detailed frameworks, real-world case studies, and governance recommendations, this book highlights applied research for generative AI in finance functions. Several chapters demonstrate how data-driven insights from AI systems can optimize complex financial processes to reduce resource usage, lower costs, and drive positive environmental impact over the long term. In addition, chapters on AI-enabled risk assessment, fraud analytics, and regulatory technology highlight applied research for generative AI in finance. The book also explores emerging applications like leveraging blockchain and metaverse interfaces to create generative AI models that can revolutionize areas from carbon credit trading to virtual audits. Overall, with in-depth applied research at the nexus of sustainability and optimization enabled by data science and generative AI, the book offers a compilation of best practices in leveraging AI for optimal, ethical, and future-oriented financial management. Audience The audience for this book is quite diverse, ranging from financial and accounting experts across banking, insurance, consultancies, regulatory agencies, and corporations seeking to enhance productivity and efficiency; business leaders want to implement ethical and compliant AI practices; researchers exploring the domain of AI and finance.



Artificial Intelligence In Mechatronics And Civil Engineering


Artificial Intelligence In Mechatronics And Civil Engineering
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Author : Ehsan Momeni
language : en
Publisher: Springer Nature
Release Date : 2023-02-15

Artificial Intelligence In Mechatronics And Civil Engineering written by Ehsan Momeni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-15 with Science categories.


Recent studies highlight the application of artificial intelligence, machine learning, and simulation techniques in engineering. This book covers the successful implementation of different intelligent techniques in various areas of engineering focusing on common areas between mechatronics and civil engineering. The power of artificial intelligence and machine learning techniques in solving some examples of real-life problems in engineering is highlighted in this book. The implementation process to design the optimum intelligent models is discussed in this book.