Synthetic Data for Deep Learning: Generate Synthetic Data for Decision Making and Applications with Python and R
暫譯: 深度學習的合成數據：使用 Python 和 R 生成決策與應用的合成數據
Gürsakal, Necmi, Çelik, Sadullah, Birişçi, Esma
- 出版商: Apress
- 出版日期: 2023-01-02
- 售價: $2,420
- 貴賓價: 9.5 折 $2,299
- 語言: 英文
- 頁數: 217
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1484285867
- ISBN-13: 9781484285862
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相關分類:
Python、程式語言、DeepLearning
海外代購書籍(需單獨結帳)
相關主題
商品描述
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.
商品描述(中文翻譯)
資料是推動政府、大型企業和運動隊等各種決策的不可或缺的燃料。其價值幾乎無法衡量。但如果這些資料無法獲取或存取有問題，該怎麼辦？這就是合成資料的用武之地。本書將向您展示如何生成合成資料並最大限度地利用它。
《深度學習的合成資料》首先追溯了合成資料的需求和發展，然後深入探討它在機器學習和計算機視覺中的角色。您將深入了解合成資料如何用於研究自動駕駛系統的優勢，以及如何對現實世界資料進行準確預測。您將通過使用 Python 和 R 的合成資料生成實際範例，將其目的和方法置於現實世界的背景中。生成對抗網絡（Generative Adversarial Networks, GANs）也將詳細介紹，解釋其工作原理及潛在應用。
完成本書後，您將具備生成和使用合成資料以增強企業、科學或政府決策所需的知識。
您將學到什麼
- 使用 R 和 Python 創建合成表格資料
- 了解合成資料對人工神經網絡的重要性
- 掌握合成資料的優勢和挑戰
- 理解與合成資料生成相關的領域隨機化（domain randomization）和領域適應（domain adaptation）等概念
本書適合誰閱讀
希望了解合成資料及其應用的人，特別是從事機器學習和計算機視覺領域的專業人士。本書對於對此主題感興趣的研究生和博士生也將非常有用。
作者簡介
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.
作者簡介(中文翻譯)
Necmi Gürsakal 是穆丹雅大學的統計學教授，他將自己的經驗和知識傳授給學生。在此之前，他在布爾薩烏魯達大學的計量經濟學系工作了超過 40 年。Necmi 發表了許多土耳其語書籍以及關於數據科學、機器學習、人工智慧、社交網絡分析和大數據的英語和土耳其語文章。此外，他還擔任過多家商業組織的顧問。
Sadullah Çelik 完成了數學的本科和研究生教育，並獲得統計學博士學位。他撰寫了大量關於大數據、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的土耳其語和英語文章。他著有三本書：《大數據》（Big Data）、《機器學習和深度學習的 R 應用線性代數》（R Applied Linear Algebra for Machine Learning and Deep Learning）和《大數據與行銷》（Big Data and Marketing）。Sadullah 目前在艾登阿德南門德雷斯大學的納齊利經濟與行政科學系及國際貿易與金融系擔任研究助理。
Esma Birişçi 是一名程式設計師、統計學家和運籌學研究員，擁有超過 15 年的計算機程式開發經驗和五年的教學經驗。她在攻讀學士學位時發展了程式設計能力，並在碩士學位課程中學習了機器學習。她的論文主題是數據增強和監督學習。Esma 轉到工業工程，並完成了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週期評估，並使用 Python 開發了一個大規模食品供應鏈系統應用。她目前在土耳其布爾薩烏魯達大學工作，將自己的知識傳授給學生。在這本書中，她自豪地能夠解釋 Python 的強大結構。
