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
Importance of Evaluation
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- A Survey on Synthetic Data Generation and Evaluation Methods, Rui Chen, Ziying Wu, Guanchen Li, Sijie Li, Jiacheng Cao, Yongqiang Zhang, and Hongmei Li, 2022 ACM Computing Surveys, Vol. 55 (Association for Computing Machinery) DOI: 10.1145/3547101 - Provides a broad overview of synthetic data generation techniques and critically examines various evaluation metrics and methodologies for assessing synthetic data quality, fidelity, and utility.
- Synthetic Data Generation for Machine Learning: A Practical Guide, Hamid Ghasemi, Mohammad Rezaei, Hamed Ahmadi, and Maryam Ghasemi, 2023 (Springer) DOI: 10.1007/978-3-031-29402-9 - This book offers a comprehensive guide, including practical considerations and methods for evaluating synthetic data for machine learning applications, covering both theoretical aspects and practical implementations.
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