Description
Chapter I: Introduction to Data 40 pages Chapter 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 Data 1.3. Definitions of Synthetic Data 1.4. The Lifecycle of Data 1.5. The Future of Data Storage 1.6. Synthetic Data and Metaverse 1.7. Computer Vision 1.8. Generating an Artificial Neural Network Using Package “nnet” in R 1.9. Understanding of Visual Scenes 1.10. Segmentation Problem 1.11. Accuracy Problems 1.12. Generative Pre-trained Transformer 3 (GPT-3) Chapter 2: Synthetic Data 40 pages Chapter 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 Data 2.2. A Brief History of Synthetic Data 2.3. Types of Synthetic Data 2.4. Benefits and Challenges of Synthetic Data 2.5. Generating Synthetic Data in A Simple Way 2.6. An Example of Biased Synthetic Data Generation 2.7. Domain Transfer 2.8. Domain Adaptation 2.9. Domain Randomization 2.10. Using Video Games to Create Synthetic Data 2.11. Synthetic Data And Autonomous Driving System 2.11.1. Perception 2.11.2. Localization 2.11.3. Prediction 2.11.4. Decision Making 2.12. Simulation in Autonomous Vehicle Companies 2.13. How to Make Automatic Data Labeling? 2.14. Is Real-World Experience Unavoidable? 2.15. Data for Learning Medical Images 2.16. Reinforcement Learning 2.17. Self-Supervised Learning Chapter 3: Synthetic Data Generation with R….. 55 pages Chapter 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 Data 3.1.1. Creating a Value Vector from a Known Univariate Distribution 3.1.2. Vector Generation from a Multi-levels Categorical Variable 3.1.3. Multivariate 3.1.4. Multivariate (with correlation) 3.2. Multivariate Imputation Via Mice Package in R 3.2.1. Example of MICE 3.3. Augmented Data 3.4. Image Augmentation Using Torch Package 3.5. Generating Synthetic Data with The “conjurer” Package in R 3.5.1. Create a Customer 3.5.2. Create a Product 3.5.3. Creating Transactions 3.5.4. Generating Synthetic Data 3.6. Generating Synthetic Data With “Synthpop” Package In R 3.7. Copula 3.7.1. t Copula 3.7.2. Normal Copula 3.7.3. Gaussian Copula Chapter 4: GANs…. 15 pages Chapter 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. GANs 4.2. CTGAN 4.3. SurfelGAN 4.4. Cycle GANs 4.5. SinGAN 4.6. DCGAN 4.7. medGAN 4.8. WGAN 4.9. seqGAN 4.10. Conditional GAN Chapter 5: Synthetic Data Generation with Python…. 40 pages Chapter 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 Distribution 5.2. Synthetic Data Generation in Regression Problem 5.3. Gaussian Noise Apply to Regression Model 5.4. Friedman Functions and Symbolic Regression 5.5. Synthetic data generation for Classification and Clustering Problems 5.6. Clustering Problems 5.7. Generation Tabular Synthetic Data by Applying GANs
