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
Finding Generation Tools
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- Synthetic Data Generation: A Review, Carla Pereira, Rui Mendes, Pedro Rodrigues, 2023 Applied Sciences, Vol. 13 (MDPI) DOI: 10.3390/app13042738 - This paper reviews various methods and applications of synthetic data generation, providing a broad overview of the field and its tool categories.
- Practical MLOps: Operationalizing Machine Learning Models, Noah Gift, Alfredo Deza, 2021 (O'Reilly Media) - This book provides guidance on selecting and integrating machine learning tools and frameworks, covering aspects like open-source choices and practical evaluation criteria, aligning with the "Evaluating Potential Tools" section.
- The Synthetic Data Vault: An Ecosystem for Privately Sharing High-Quality Synthetic Data, Neha Gupta, Kalyan Veeramachaneni, Paul Van Horn, Vivek F. Farias, 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD) (ACM (Association for Computing Machinery)) DOI: 10.1145/3318464.3380720 - This paper introduces the Synthetic Data Vault (SDV), a prominent ecosystem for generating synthetic data, serving as an example of a sophisticated tool and its underlying research.