Introduction to Synthetic Data for Machine Learning
Chapter 1: Understanding Synthetic Data
What is Synthetic Data?
Why Generate Artificial Data?
Real Data vs. Synthetic Data
Common Terminology
Potential Benefits
General Limitations
Quiz for Chapter 1
Chapter 2: Basic Methods for Data Generation
The Idea of Data Generation Models
Generating Data from Statistical Distributions
Introduction to Rule-Based Systems
Generating Simple Numerical Data
Generating Simple Categorical Data
Hands-on Practical: Create Basic Synthetic Data
Quiz for Chapter 2
Chapter 3: Generating Synthetic Tabular Data
Understanding Tabular Data Structure
Row Sampling Techniques
Independent Column Value Generation
Preserving Basic Column Correlations
Introduction to Data Anonymization Concepts
Hands-on Practical: Generate a Synthetic Table
Quiz for Chapter 3
Chapter 4: Introduction to Synthetic Image Data
Why Synthetic Data for Images?
Basic Image Properties: Pixels and Color
Creating Images with Simple Shapes and Patterns
Applying Noise and Simple Augmentations
Introduction to Rendering Simple Scenes
Challenges in Realistic Image Generation
Hands-on Practical: Generate Simple Synthetic Images
Quiz for Chapter 4
Chapter 5: Evaluating Synthetic Data Quality
Importance of Evaluation
Visual Inspection Methods
Basic Statistical Comparisons
Comparing Data Distributions
Concept of Fidelity vs. Utility
Quiz for Chapter 5
Chapter 6: Tools and Libraries Overview
Role of Software in Data Generation
Libraries for Basic Data Manipulation (NumPy, Pandas)
Introduction to Faker Library
Libraries for Simple Image Manipulation (Pillow, Scikit-image)
Finding Generation Tools
Quiz for Chapter 6
What is Synthetic Data?
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- Generating privacy-preserving synthetic data: A comprehensive review and a case study, Jing Weng, Peijian Sun, Xianchao Li, Cun Liu, Yichuan Zhang, 2022 Journal of Network and Computer Applications, Vol. 210 (Elsevier Ltd.) DOI: 10.1016/j.jnca.2022.103328 - This review focuses on the methods and practices for generating synthetic data while preserving privacy. It is useful for understanding the role of synthetic data in addressing privacy concerns.
- Measuring the Utility and Privacy of Synthetic Data, J. D. O'Malley, C. J. O'Malley, M. H. S. O'Malley, and J. M. O'Malley, 2020 Information, Vol. 11 (MDPI) DOI: 10.3390/info11100478 - This paper discusses ways to measure the quality of synthetic data, considering both its usefulness and privacy protection. It helps in assessing how well synthetic data emulates real data.