Synthetic data for deep learning

• Verfasst von: Gürsakal, Necmi [VerfasserIn]
• Verfasst von: Çelik, Sadullah [VerfasserIn]
• Verfasst von: Birişçi, Esma [VerfasserIn]
• Titel: Synthetic data for deep learning
• Titelzusatz: generate synthetic data for decision making and applications with python and R
• Verf.angabe: Necmi Gürsakal, Sadullah Çelik, Esma Birişçi
• Verlagsort: New York, NY
• Verlag: Apress
• E-Jahr: 2022
• Jahr: [2022]
• Umfang: xix, 220 Seiten
• Illustrationen: Illustrationen, Diagramme (farbig)
• ISBN: 978-1-4842-8586-2
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Online-Ausgabe: Gürsakal, Necmi, 1950 - : Synthetic data for deep learning. - New York : Apress, 2022. - 1 Online-Ressource (xix, 220 Seiten)
• Sach-SW: Artificial intelligence
• Sach-SW: COMPUTERS / Artificial Intelligence
• Sach-SW: COMPUTERS / General
• Sach-SW: COMPUTERS / Programming Languages / Python
• Sach-SW: Computing & information technology
• Sach-SW: Künstliche Intelligenz
• Sach-SW: Machine learning
• Sach-SW: Maschinelles Lernen
• Sach-SW: Programmier- und Skriptsprachen, allgemein
• Sach-SW: Programming & scripting languages: general
• K10plus-PPN: 1830204653

Abstract

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 LearnCreate synthetic tabular data with R and PythonUnderstand how synthetic data is important for artificial neural networksMaster the benefits and challenges of synthetic dataUnderstand concepts such as domain randomization and domain adaptation related to synthetic data generationWho This Book Is ForThose 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