Information-Driven Machine Learning

Information-Driven Machine Learning portes grátis

Information-Driven Machine Learning

Data Science as an Engineering Discipline

Friedland, Gerald

Springer International Publishing AG

12/2023

267

Dura

Inglês

9783031394768

15 a 20 dias

Descrição não disponível.
Preface.- 1 Introduction.- 2 The Automated Scientific Process.- 3 The (Black Box) Machine Learning Process.- 4 Information Theory.- 5 Capacity.- 6 The Mechanics of Generalization.- 7 Meta-Math: Exploring the Limits of Modeling.- 8 Capacity of Neural Networks.- 8 Capacity of Neural Networks.- 10 Capacities of some other Machine Learning Methods.- 11 Data Collection and Preparation.- 12 Measuring Data Sufficiency.- 13 Machine Learning Operations.- 14 Explainability.- 15 Repeatability and Reproducibility.- 16 The Curse of Training and the Blessing of High Dimensionality.- 16 The Curse of Training and the Blessing of High Dimensionality.- Appendix A Recap: The Logarithm.- Appendix B More on Complexity.- Appendix C Concepts Cheat Sheet.- Appendix D A Review Form that Promotes Reproducibility.- List of Illustrations.- Bibliography.
machine learning experiments;information theory;information measurements;decision trees;neural networks;explainability