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
General Limitations
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- Synthetic Data Generation: A Review, Shanshan Wang, Yuze Li, Haifeng Wang, Xing Xie, Yong Li, 2021 IEEE Transactions on Big Data, Vol. 9 (IEEE) DOI: 10.1109/TBDATA.2021.3134976 - This review article details various synthetic data generation methods and evaluates their limitations, including data fidelity, privacy concerns, and potential biases.
- The Reality Gap in Synthetic Data Generation, Avi Frid, Miri Dagan, 2021 IEEE Transactions on Knowledge and Data Engineering, Vol. 34 (IEEE) DOI: 10.1109/TKDE.2021.3129486 - This paper focuses on the 'reality gap' challenge, where synthetic data fails to fully capture the subtle features of real data, potentially affecting downstream model performance.
- How to Measure the Quality of Synthetic Data: A Survey, Eunsun Kim, Jinsuk Kim, Seungjoo Lee, Minsun Kim, 2021 Applied Sciences, Vol. 11 DOI: 10.3390/app112110186 - This survey offers an overview of metrics and methods for assessing the fidelity and utility of synthetic data, directly addressing the complexities of its validation.