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
Preserving Basic Column Correlations
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- Synthetic Data Generation for Tabular Data: A Survey, Jing Sun, Shuyue Wang, Ze Shi, Guang-Lei Chen, 2023 ACM Computing Surveys, Vol. 56 (Association for Computing Machinery) DOI: 10.1145/3630248 - This survey reviews methods for generating synthetic tabular data, discussing the importance of preserving data distributions and inter-column relationships for data utility.
- Modeling Tabular Data using Conditional GAN, Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni, 2019 Advances in Neural Information Processing Systems, Vol. 32 (Curran Associates, Inc.) DOI: 10.48550/arXiv.1907.00503 - This paper introduces CTGAN, highlighting the difficulties of generating high-fidelity synthetic tabular data that maintains complex, multi-modal, and inter-column relationships.
- Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists, Alice Zheng, Amanda Casari, 2018 (O'Reilly Media) - This book offers practical advice on preparing data for machine learning, including understanding feature relationships and their influence on model performance.
- Synthetic Data: Generating and Evaluating, Bas Van Breugel, Marc J. D. van der Weide, 2021 IEEE Security & Privacy, Vol. 19 (IEEE) DOI: 10.1109/MSEC.2021.3059170 - This article gives an overview of synthetic data generation and assessment, stressing the significance of statistical fidelity, particularly inter-variable relationships, for the synthetic data's value.