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…mehr
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.
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.
Inhaltsangabe
1. Machine Learning Cloud Regression and Optimization 2. A Simple, Robust and Efficient Ensemble Method 3. Gentle Introduction to Linear Algebra - Synthetic Time Series 4. Image and Video Generation 5. Synthetic Clusters and Alternative to GMM 6. Shape Classification and Synthetization via Explainable AI 7. Synthetic Data, Interpretable Regression, and Submodels 8. From Interpolation to Fuzzy Regression 9. New Interpolation Methods for Synthetization and Prediction 10. Synthetic Tabular Data: Copulas vs enhanced GANs 11. High Quality Random Numbers for Data Synthetization 12. Some Unusual Random Walks 13. Divergent Optimization Algorithm and Synthetic Functions 14. Synthetic Terrain Generation and AI-generated Art 15. Synthetic Star Cluster Generation with Collision Graphs 16. Perturbed Lattice Point Process: Alternative to GMM 17. Synthetizing Multiplicative Functions in Number Theory 18. Text, Sound Generation and Other Topics
1. Machine Learning Cloud Regression and Optimization 2. A Simple, Robust and Efficient Ensemble Method 3. Gentle Introduction to Linear Algebra - Synthetic Time Series 4. Image and Video Generation 5. Synthetic Clusters and Alternative to GMM 6. Shape Classification and Synthetization via Explainable AI 7. Synthetic Data, Interpretable Regression, and Submodels 8. From Interpolation to Fuzzy Regression 9. New Interpolation Methods for Synthetization and Prediction 10. Synthetic Tabular Data: Copulas vs enhanced GANs 11. High Quality Random Numbers for Data Synthetization 12. Some Unusual Random Walks 13. Divergent Optimization Algorithm and Synthetic Functions 14. Synthetic Terrain Generation and AI-generated Art 15. Synthetic Star Cluster Generation with Collision Graphs 16. Perturbed Lattice Point Process: Alternative to GMM 17. Synthetizing Multiplicative Functions in Number Theory 18. Text, Sound Generation and Other Topics
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