SYNTHETIC DATA AND GENERATIVE AI / Vincent Granville

Von:

Granville, Vincent [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Verlag: Cambridge, MA : MORGAN KAUFMANN, 2024Auflage: First editionBeschreibung: 1 Online-RessourceISBN:

9780443218569
0443218560
0443218579
9780443218576

Schlagwörter:

Andere physische Formen: Erscheint auch als: 0443218579 Druck-AusgabeDDC-Klassifikation:

006.3/1 23/eng/20240116

LOC-Klassifikation:

Q325.5

Online-Ressourcen:

Zugang im Netz des KIT

Zusammenfassung: 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 gravityPPN: PPN: 1907960031Package identifier: Produktsigel: ZDB-4-NLEBK | BSZ-4-NLEBK-KAUB

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