Synthetic Data and Generative AI

by Vincent Granville

Overview

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

Product Details

ISBN-13:	9780443218569
Publisher:	Morgan Kaufmann Publishers
Publication date:	01/09/2024
Sold by:	Barnes & Noble
Format:	eBook
Pages:	408
File size:	113 MB
Note:	This product may take a few minutes to download.

About the Author

Dr. Vincent Granville is a pioneering data scientist and machine learning expert, co-founder of Data Science Central (acquired by TechTarget in 2020), founder of MLTechniques.com, former VC-funded executive, author, and patent owner. Dr. Granville's past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET. Dr. Granville is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS). Dr. Granville has published in Journal of Number Theory, Journal of the Royal Statistical Society, and IEEE Transactions on Pattern Analysis and Machine Intelligence, and he is the author of Developing Analytic Talent: Becoming a Data Scientist, Wiley. Dr. Granville lives in Washington state, and enjoys doing research on stochastic processes, dynamical systems, experimental math, and probabilistic number theory. He has been listed in the Forbes magazine Top 20 Big Data Influencers.
Dr. Vincent Granville is a pioneering data scientist and machine learning expert, co-founder of Data Science Central (acquired by TechTarget in 2020), founder of MLTechniques.com, former VC-funded executive, author, and patent owner. Dr. Granville’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET. Dr. Granville is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS). Dr. Granville has published in Journal of Number Theory, Journal of the Royal Statistical Society, and IEEE Transactions on Pattern Analysis and Machine Intelligence, and he is the author of Developing Analytic Talent: Becoming a Data Scientist, Wiley. Dr. Granville lives in Washington state, and enjoys doing research on stochastic processes, dynamical systems, experimental math, and probabilistic number theory. He has been listed in the Forbes magazine Top 20 Big Data Influencers.

Table of Contents

1. Machine Learning Cloud Regression and Optimization2. A Simple, Robust and Efficient Ensemble Method3. Gentle Introduction to Linear Algebra – Synthetic Time Series4. Image and Video Generation5. Synthetic Clusters and Alternative to GMM6. Shape Classification and Synthetization via Explainable AI7. Synthetic Data, Interpretable Regression, and Submodels8. From Interpolation to Fuzzy Regression9. New Interpolation Methods for Synthetization and Prediction10. Synthetic Tabular Data: Copulas vs enhanced GANs11. High Quality Random Numbers for Data Synthetization12. Some Unusual Random Walks13. Divergent Optimization Algorithm and Synthetic Functions14. Synthetic Terrain Generation and AI-generated Art15. Synthetic Star Cluster Generation with Collision Graphs16. Perturbed Lattice Point Process: Alternative to GMM17. Synthetizing Multiplicative Functions in Number Theory18. Text, Sound Generation and Other Topics

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From the Publisher

Provides a comprehensive look at the foundations of machine learning, including modern approaches to solving complex problems