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

✍ Scribed by Necmi Gürsakal, Sadullah Çelik, Esma Birişçi

• Publisher: Apress
• Tongue: English
• Leaves: 235
• Category: Library

✦ Synopsis

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

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