Synthetic Data and Generative AI

Vincent Granville

• Publisher: Morgan Kaufmann
• Year: 2024
• Print ISBN:
  9780443218576
• eBook ISBN:
  9780443218569
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Synthetic Data and Generative AI covers the foundations of machine learning, with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including logistic and Lasso – are presented as a single method, without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap, without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods. - Emphasizes numerical stability and performance of algorithms (computational complexity) - Focuses on explainable AI/interpretable machine learning, with heavy use of synthetic data and generative models, a new trend in the field - Includes new, easier construction of confidence regions, without statistics, a simple alternative to the powerful, well-known XGBoost technique - Covers automation of data cleaning, favoring easier solutions when possible - Includes chapters dedicated fully to synthetic data applications: fractal-like terrain generation with the diamond-square algorithm, and synthetic star clusters evolving over time and bound by gravity

Table of contents

• Cover
• Front Matter
• Table of Contents
• Copyright
• Contents
• List of Illustrations
• List of Tables
• Chapter 1 : Machine learning cloud regression and optimization: The Swiss army knife of optimization
• Chapter 2 : A simple, robust, and efficient ensemble method: Application to natural language processing
• Chapter 3 : Gentle introduction to linear algebra – synthetic time series
• Chapter 4 : Image and video generation: The art of visualizing high-dimensional data
• Chapter 5 : Synthetic clusters and alternative to GMM: Fast classification and clustering via image convolution filters
• Chapter 6 : Shape classification and synthetization via explainable AI
• Chapter 7 : Synthetic data, interpretable regression, and submodels: Little known secrets about linear regression
• Chapter 8 : From interpolation to fuzzy regression
• Chapter 9 : New interpolation methods for synthetization and prediction
• Chapter 10 : Synthetic tabular data: copulas vs enhanced GANs
• Chapter 11 : High quality random numbers for data synthetization
• Chapter 12 : Some unusual random walks: Testing and leveraging quasirandomness
• Chapter 13 : Divergent optimization algorithm and synthetic functions: When all else fails
• Chapter 14 : Synthetic terrain generation and AI-generated art
• Chapter 15 : Synthetic star cluster generation with collision graphs
• Chapter 16 : Perturbed lattice point process: alternative to GMM: Inference, nearest neighbor graph
• Chapter 17 : Synthetizing multiplicative functions in number theory: New perspective on the Riemann Hypothesis
• Chapter 18 : Text, sound generation, and other topics
• Glossary
• Index
• Symbols