Description
Book SynopsisData 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
Table of Contents
Chapter I: Introduction to Data 40 pagesChapter Goal: The book section entitled "Data" aims to provide readers with information on the history, definition, and future of data storage, as well as the role that synthetic data can play in the field of computer vision. 1.1. The History of Data1.3. Definitions of Synthetic Data1.4. The Lifecycle of Data1.5. The Future of Data Storage1.6. Synthetic Data and Metaverse1.7. Computer Vision1.8. Generating an Artificial Neural Network Using Package “nnet” in R1.9. Understanding of Visual Scenes1.10. Segmentation Problem1.11. Accuracy Problems1.12. Generative Pre-trained Transformer 3 (GPT-3)
Chapter 2: Synthetic Data 40 pagesChapter Goal: The purpose of this chapter is to provide information about synthetic data and how it can be used to benefit autonomous driving systems. Synthetic data is a term used to describe data that has been generated by a computer. 2.1. Synthetic Data2.2. A Brief History of Synthetic Data2.3. Types of Synthetic Data2.4. Benefits and Challenges of Synthetic Data2.5. Generating Synthetic Data in A Simple Way2.6. An Example of Biased Synthetic Data Generation2.7. Domain Transfer2.8. Domain Adaptation2.9. Domain Randomization2.10. Using Video Games to Create Synthetic Data2.11. Synthetic Data And Autonomous Driving System2.11.1. Perception2.11.2. Localization2.11.3. Prediction2.11.4. Decision Making2.12. Simulation in Autonomous Vehicle Companies2.13. How to Make Automatic Data Labeling? 2.14. Is Real-World Experience Unavoidable? 2.15. Data for Learning Medical Images2.16. Reinforcement Learning2.17. Self-Supervised Learning
Chapter 3: Synthetic Data Generation with R..... 55 pagesChapter Goal: The purpose of this book section is to provide information about the content and purpose of synthetic data generation with R. Synthetic data is generated data that is used to mimic real data. There are many reasons why one might want to generate synthetic data. For example, synthetic data can be used to test data-driven models when real data is not available. Synthetic data can also be used to protect the privacy of individuals in data sets.3.1. Basic Functions Used In Generating Synthetic Data3.1.1. Creating a Value Vector from a Known Univariate Distribution3.1.2. Vector Generation from a Multi-levels Categorical Variable3.1.3. Multivariate3.1.4. Multivariate (with correlation) 3.2. Multivariate Imputation Via Mice Package in R3.2.1. Example of MICE3.3. Augmented Data3.4. Image Augmentation Using Torch Package3.5. Generating Synthetic Data with The "conjurer" Package in R3.5.1. Create a Customer3.5.2. Create a Product3.5.3. Creating Transactions3.5.4. Generating Synthetic Data3.6. Generating Synthetic Data With “Synthpop” Package In R3.7. Copula3.7.1. t Copula3.7.2. Normal Copula3.7.3. Gaussian Copula
Chapter 4: GANs.... 15 pagesChapter Goal: This book chapter aims to provide information on the content and purpose of GANs. GANs are a type of artificial intelligence that is used to generate new data that is similar to the training data. This is done by training a generator network to produce data that is similar to the training data. The generator network is trained by using a discriminator network, which is used to distinguish between the generated data and the training data. 4.1. GANs4.2. CTGAN4.3. SurfelGAN4.4. Cycle GANs4.5. SinGAN4.6. DCGAN4.7. medGAN4.8. WGAN4.9. seqGAN4.10. Conditional GAN
Chapter 5: Synthetic Data Generation with Python.... 40 pagesChapter Goal: The purpose of this chapter is to provide information about the methods of synthetic data generation with Python. Python is a widely used high-level programming language that is known for its ease of use and readability. It has a large standard library that covers a wide range of programming tasks.5.1. Data Generation with Know Distribution5.2. Synthetic Data Generation in Regression Problem5.3. Gaussian Noise Apply to Regression Model5.4. Friedman Functions and Symbolic Regression5.5. Synthetic data generation for Classification and Clustering Problems5.6. Clustering Problems5.7. Generation Tabular Synthetic Data by Applying GANs
