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

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.

數據是推動政府、大型企業和運動隊等各個領域的決策的不可或缺的燃料。它的價值幾乎無法估量。但如果該數據無法獲取或存在問題，該怎麼辦？這就是合成數據的用途。本書將向您展示如何生成合成數據並充分利用它。

《深度學習的合成數據》首先追溯了合成數據的需求和發展，然後深入探討了它在機器學習和計算機視覺中的作用。您將瞭解合成數據如何用於研究自動駕駛系統的優勢以及對真實世界數據的準確預測。您將通過使用Python和R進行合成數據生成的實際示例，將其目的和方法置於現實世界的背景中。書中還詳細介紹了生成對抗網絡（GANs），解釋了它們的工作原理和潛在應用。

完成本書後，您將具備生成和使用合成數據以增強企業、科學或政府決策的知識。

您將學到以下內容：
- 使用R和Python創建合成表格數據
- 瞭解合成數據對人工神經網絡的重要性
- 掌握合成數據的優勢和挑戰
- 瞭解與合成數據生成相關的域隨機化和域適應等概念

本書適合對合成數據及其應用感興趣的人，尤其是從事機器學習和計算機視覺領域的專業人士。本書也對對此主題感興趣的研究生和博士生有用。

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.

Necmi Gürsakal是Mudanya大學的統計學教授，他將自己的經驗和知識傳授給學生。在此之前，他在Bursa Uludag大學計量經濟學系擔任教職員超過40年。Necmi在數據科學、機器學習、人工智能、社交網絡分析和大數據方面發表了許多土耳其書籍和英文、土耳其文章。此外，他還擔任過多個商業組織的顧問。

Sadullah Çelik在數學方面完成了本科和研究生教育，並在統計學方面獲得了博士學位。他在大數據、數據科學、機器學習、生成對抗網絡（GANs）、多變量統計和網絡科學方面撰寫了許多土耳其和英文文章。他撰寫了三本書：《大數據》、《應用於機器學習和深度學習的R線性代數》和《大數據與營銷》。Sadullah目前在Aydın Adnan Menderes大學Nazilli經濟與行政科學系和國際貿易與金融系擔任研究助理。

Esma Birişçi是一位具有超過15年計算機程序開發經驗和5年教學經驗的程序員、統計學家和運營研究員。她在攻讀學士學位期間發展了自己的編程能力，在碩士學位課程中學習了機器學習知識。她的論文研究了數據擴增和監督學習。Esma轉到工業工程專業，完成了關於動態和隨機非線性規劃的博士學位課程。她研究了大規模優化和生命周期評估，並使用Python開發了一個大規模食品供應鏈系統應用。她目前在土耳其的Bursa Uludag大學工作，將自己的知識傳授給學生。在這本書中，她很自豪能夠解釋Python的強大結構。