Vincent Granville,
Vincent (Author and Publisher, MLTechniques.com, USA) Granville
Synthetic Data and Generative AI
Paperback Engels 2024 9780443218576
Verwachte levertijd ongeveer 9 werkdagen
Samenvatting
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.
Specificaties
Lezersrecensies
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Inhoudsopgave
1. Machine Learning Cloud Regression and Optimization<br>2. A Simple, Robust and Efficient Ensemble Method<br>3. Gentle Introduction to Linear Algebra – Synthetic Time Series<br>4. Image and Video Generation<br>5. Synthetic Clusters and Alternative to GMM<br>6. Shape Classification and Synthetization via Explainable AI<br>7. Synthetic Data, Interpretable Regression, and Submodels<br>8. From Interpolation to Fuzzy Regression<br>9. New Interpolation Methods for Synthetization and Prediction<br>10. Synthetic Tabular Data: Copulas vs enhanced GANs<br>11. High Quality Random Numbers for Data Synthetization<br>12. Some Unusual Random Walks<br>13. Divergent Optimization Algorithm and Synthetic Functions<br>14. Synthetic Terrain Generation and AI-generated Art<br>15. Synthetic Star Cluster Generation with Collision Graphs<br>16. Perturbed Lattice Point Process: Alternative to GMM<br>17. Synthetizing Multiplicative Functions in Number Theory<br>18. Text, Sound Generation and Other Topics
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