Extent:
1 Online-Ressource (XVII, 221 Seiten)
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