Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- Part I Fundamental tools and concepts
- Part II Tools for fully data-driven machine learning
- Part III Methods for large scale machine learning
- Part IV Appendices
- A Basic vector and matrix operations
- B Basics of vector calculus
- C Fundamental matrix factorizations and the pseudo-inverse
- D Convex geometry
- References
- Index
D - Convex geometry
from Part IV - Appendices
- Frontmatter
- Contents
- Preface
- 1 Introduction
- Part I Fundamental tools and concepts
- Part II Tools for fully data-driven machine learning
- Part III Methods for large scale machine learning
- Part IV Appendices
- A Basic vector and matrix operations
- B Basics of vector calculus
- C Fundamental matrix factorizations and the pseudo-inverse
- D Convex geometry
- References
- Index
Summary
- Type
- Chapter
- Information
- Machine Learning RefinedFoundations, Algorithms, and Applications, pp. 278 - 279Publisher: Cambridge University PressPrint publication year: 2016