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
This chapter is part of a book that is no longer available to purchase from Cambridge Core
A - Basic vector and matrix operations
from Part IV - Appendices
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
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- Machine Learning RefinedFoundations, Algorithms, and Applications, pp. 265 - 267Publisher: Cambridge University PressPrint publication year: 2016