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
Applying Noise and Simple Augmentations
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - This foundational textbook includes sections explaining data augmentation techniques as a method for regularization and improving model generalization in deep learning.
- A Survey on Data Augmentation for Deep Learning: From Basic Transformations to Meta-Learning, Conner Shorten and Taghi M. Khoshgoftaar, 2019 Journal of Artificial Intelligence Research, Vol. 66 (AI Access Foundation) DOI: 10.1613/jair.11652 - This comprehensive survey reviews various data augmentation techniques, including the basic transformations (noise, geometric, color) discussed in the section, providing a broad overview of their application in deep learning.
- Albumentations Documentation, Albumentations Team, Ongoing - Official documentation for a widely used image augmentation library, detailing various noise types, geometric, and color transformations with practical examples.
- torchvision.transforms - PyTorch Documentation, PyTorch Developers, Ongoing - Official documentation for PyTorch's image transformation module, providing definitions and usage for common augmentations like resizing, cropping, rotation, and noise for deep learning tasks.