000			01510nam a2200253 4500
005			20250613150741.0
008			250613b |||||||| |||| 00| 0 eng d
020			_a9780443218576
040			_aCSL _cCSL
041			_2eng _aeng
084			_aD65,8(B) R4 _qCSL
100			_aGranville, Vincent _eauthor. _9479071
245			_aSynthetic Data and Generative AI
260			_aCambridge: _bMorgan Kaufmann, _c2024.
300			_axii, 396p. _b: col. ill. _c; 23 cm
500			_aIncludes Glossary and index
520			_aSynthetic 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.
650			_aArtificial intelligence
650			_aMachine learning _9480917
650			_aGenerative adversarial networks (GANs) _9812849
650			_aData mining
650			_aComputer simulation _9713688
942			_2CC _n0 _cTEXL _hD65,8(B) R4
999			_c1431718 _d1431718