Synthetic Data for Deep Learning: Generate Synthetic Data for Decision Making and Applications with Python and R

Gürsakal, Necmi; à elik, Sadullah; BiriŠçi, Esma

ISBN 10: 1484285867 / ISBN 13: 9781484285862

Verlag: Apress (edition 1st ed.), 2023

Beschreibung

It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Bestandsnummer des Verkäufers 1484285867-8-1

Bibliografische Details

• Titel: Synthetic Data for Deep Learning: Generate Synthetic Data for Decision Making and Applications with Python and R
• Verlag: Apress (edition 1st ed.)
• Erscheinungsjahr: 2023
• Sprache: Englisch
• ISBN-10: 1484285867
• ISBN-13: 9781484285862
• Einband: Paperback
• Auflage: 1st ed.
• Zustand: Very Good

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

Necmi Gürsakal is a statistics professor at Mudanya University, where he transfers his experience and knowledge to his students. Before that, he worked as a faculty member at the Bursa Uludag University Econometrics Department for more than 40 years. Necmi has many published Turkish books and English and Turkish articles on data science, machine learning, artificial intelligence, social network analysis, and big data. In addition, he has served as a consultant to various business organizations.

Sadullah Çelik completed his undergraduate and graduate education in mathematics and his doctorate in statistics. He has written numerous Turkish and English articles on big data, data science, machine Learning, Generative Adversarial Networks (GANs), multivariate statistics, and network science. He has authored three books: Big Data, R Applied Linear Algebra for Machine Learning and Deep Learning, and Big Data and Marketing. Sadullah is currently working as Research Assistant at Aydın Adnan Menderes University, Nazilli Department of Economics and Administrative Sciences, and Department of International Trade and Finance.
Esma Birişçi is a programmer, statistician, and operations researcher with more than 15 years of experience in computer program development and five years in teaching students. She developed her programming ability while studying for her bachelor degree, and knowledge of machine learning during her master degree program. She completed her thesis about data augmentation and supervised learning. Esma transferred to Industrial Engineering and completed her doctorate program on dynamic and stochastic nonlinear programming. She studied large-scale optimization and life cycle assessment, and developed a large-scale food supply chain system application using Python. She is currently working at Bursa Uludag University, Turkey, where she transfers her knowledge to students. In this book, she is proud to be able to explain Python's powerful structure.