Synthetic data for deep learning : generate synthetic data for decision making and applications with Python and R
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Format
Language
English
ISBN
9781484285879, 1484285875
UPC
10.1007/978-1-4842-8587-9
Notes
Bibliography
Includes bibliographical references and index.
Description
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.
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O'Reilly,O'Reilly Online Learning: Academic/Public Library Edition
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Citations
APA Citation, 7th Edition (style guide)
Gürsakal, N., Celik, S., & Biris̨c̨i, E. (2022). Synthetic data for deep learning: generate synthetic data for decision making and applications with Python and R . Apress.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Gürsakal, Necmi, Sadullah, Celik and Esma, Biris̨c̨i. 2022. Synthetic Data for Deep Learning: Generate Synthetic Data for Decision Making and Applications With Python and R. Apress.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Gürsakal, Necmi, Sadullah, Celik and Esma, Biris̨c̨i. Synthetic Data for Deep Learning: Generate Synthetic Data for Decision Making and Applications With Python and R Apress, 2022.
MLA Citation, 9th Edition (style guide)Gürsakal, Necmi,, Sadullah Celik, and Esma Biris̨c̨i. Synthetic Data for Deep Learning: Generate Synthetic Data for Decision Making and Applications With Python and R Apress, 2022.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
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Grouped Work ID
f6665ede-1427-2027-9367-a1fc3957cd15-eng
Grouping Information
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|---|---|
| Full title | synthetic data for deep learning generate synthetic data for decision making and applications with python and r |
| Author | gürsakal necmi |
| Grouping Category | book |
| Last Update | 2024-03-29 07:51:22AM |
| Last Indexed | 2024-05-16 02:50:56AM |
Book Cover Information
| Image Source | contentCafe |
|---|---|
| First Loaded | Dec 8, 2023 |
| Last Used | Dec 8, 2023 |
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| 245 | 1 | 0 | |a Synthetic data for deep learning :|b generate synthetic data for decision making and applications with Python and R /|c Necmi Gürsakal, Sadullah Celik, Esma Biris̨c̨i. |
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Preface -- Introduction -- Chapter 1: An Introduction to Synthetic Data -- What Synthetic Data is? -- Why is Synthetic Data Important? -- Synthetic Data for Data Science and Artificial Intelligence -- Accuracy Problems -- The Lifecycle of Data -- Data Collection versus Privacy -- Data Privacy and Synthetic Data -- The Bottom Line -- Synthetic Data and Data Quality -- Aplications of Synthetic Data -- Financial Services -- Manufacturing -- Healthcare -- Automotive -- Robotics -- Security -- Social Media | |
| 505 | 8 | |a Marketing -- Natural Language Processing -- Computer Vision -- Understanding of Visual Scenes -- Segmentation Problem -- Summary -- References -- Chapter 2: Foundations of Synthetic data -- How to Generated Fair Synthetic Data? -- Generating Synthetic Data in A Simple Way -- Using Video Games to Create Synthetic Data -- The Synthetic-to-Real Domain Gap -- Bridging the Gap -- Domain Transfer -- Domain Adaptation -- Domain Randomization -- Is Real-World Experience Unavoidable? -- Pretraining -- Reinforcement Learning -- Self-Supervised Learning -- Summary -- References | |
| 505 | 8 | |a Chapter 3: Introduction to GANs -- GANs -- CTGAN -- SurfelGAN -- Cycle GANs -- SinGAN-Seg -- MedGAN -- DCGAN -- WGAN -- SeqGAN -- Conditional GAN -- BigGAN -- Summary -- References -- Chapter 4: Synthetic Data Generation with R -- Basic Functions Used in Generating Synthetic Data -- Creating a Value Vector from a Known Univariate Distribution -- Vector Generation from a Multi-Levels Categorical Variable -- Multivariate -- Multivariate (with correlation) -- Generating an Artificial Neural Network Using Package "nnet" in R -- Augmented Data -- Image Augmentation Using Torch Package | |
| 505 | 8 | |a Multivariate Imputation Via "mice" Package in R -- Generating Synthetic Data with the "conjurer" Package in R -- Creat a Customer -- Creat a Product -- Creating Transactions -- Generating Synthetic Data -- Generating Synthetic Data with "Synthpop" Package In R -- Copula -- t Copula -- Normal Copula -- Gaussian Copula -- Summary -- References -- Chapter 5: Synthetic Data Generation with Python -- Data Generation with Know Distribution -- Data with Date information -- Data with Internet information -- A more complex and comprehensive example -- Synthetic Data Generation in Regression Problem | |
| 505 | 8 | |a Gaussian Noise Apply to Regression Model -- Friedman Functions and Symbolic Regression -- Make 3d Plot -- Make3d Plot -- Synthetic data generation for Classification and Clustering Problems -- Classification Problems -- Clustering Problems -- Generation Tabular Synthetic Data by Applying GANs -- Synthetic data Generation -- Summary -- Reference -- Index | |
| 520 | |a 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. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on January 23, 2023). | ||
| 590 | |a O'Reilly|b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning.|9 46043 | |
| 650 | 0 | |a Computer vision.|9 34219 | |
| 655 | 0 | |a Electronic books. | |
| 700 | 1 | |a Celik, Sadullah,|e author. | |
| 700 | 1 | |a Biris̨c̨i, Esma,|e author. | |
| 776 | 0 | 8 | |i Print version:|z 1484285867|z 9781484285862|w (OCoLC)1322811904 |
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