Synthetic Data for Deep Learning: Generate Synthetic Data for Decision Making and Applications with Python and R
- Length: 239 pages
- Edition: 1
- Language: English
- Publisher: Apress
- Publication Date: 2022-12-31
- ISBN-10: 1484285867
- ISBN-13: 9781484285862
- Sales Rank: #5546917 (See Top 100 Books)
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.
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 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 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 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 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
Donate to keep this site alive
How to download source code?
1. Go to: https://github.com/Apress
2. In the Find a repository… box, search the book title: Synthetic Data for Deep Learning: Generate Synthetic Data for Decision Making and Applications with Python and R
, sometime you may not get the results, please search the main title.
3. Click the book title in the search results.
3. Click Code to download.
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.