Extent: | 1 Online-Ressource (XVII, 221 Seiten) |
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Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Frontmatter Acknowledgments Contents Preface 1 Introduction Part I: Machine Learning I 2 Simple Linear Regression – Concept 3 Simple Linear Regression – Theory 4 Simple Linear Regression – Practice Part II: Model Assessment 8 Model Assessment – Bias-Variance Tradeoff 9 Model Assessment – Regression 10 Model Assessment – Classification Part III: Machine Learning II 11 Multiple Linear Regression – Concept 12 Multiple Linear Regression – Theory 13 Multiple Linear Regression – Practice 14 Logistic Regression – Concept 15 Logistic Regression – Theory 16 Logistic Regression – Practice Part IV: Deep Learning 20 Deep Learning – Bird’s Eye View 21 Neurons 22 Neurons – Practice 23 Network Architecture 24 Network Architecture – Practice 25 Forward Propagation 26 Forward Propagation – Practice 27 Loss Function 28 Loss Function – Practice 29 Backward Propagation 30 Backward Propagation – Practice 31 Deep Learning – Practice List of Figures List of Tables About the Authors Index In English |
ISBN: | 978-1-5015-0573-7 ; 978-1-5015-0584-3 ; 978-1-5015-1464-7 |
Other identifiers: | 10.1515/9781501505737 [DOI] |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014550437