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Mathematics of Machine Learning

You're reading from Mathematics of Machine Learning Master linear algebra, calculus, and probability for machine learning

Product type Paperback

Published in May 2025

Publisher Packt

ISBN-13 9781837027873

Length 730 pages

Edition 1st Edition

Concepts

Machine Learning

Author (1):

Tivadar Danka

View More author details

Table of Contents (36) Chapters

Introduction

Part 1: Linear Algebra FREE CHAPTER

1 Vectors and Vector Spaces

2 The Geometric Structure of Vector Spaces

3 Linear Algebra in Practice

4 Linear Transformations

5 Matrices and Equations

6 Eigenvalues and Eigenvectors

7 Matrix Factorizations

8 Matrices and Graphs

References

Part 2: Calculus

9 Functions

10 Numbers, Sequences, and Series

11 Topology, Limits, and Continuity

12 Differentiation

13 Optimization

14 Integration

References

Part 3: Multivariable Calculus

15 Multivariable Functions

16 Derivatives and Gradients

17 Optimization in Multiple Variables

References

Part 4: Probability Theory

18 What is Probability?

19 Random Variables and Distributions

20 The Expected Value

References

Part 5: Appendix

Other Books You May Enjoy

Index

Appendix A It’s Just Logic

1. Appendix B The Structure of Mathematics

2. Appendix C Basics of Set Theory

3. Appendix D Complex Numbers

Linear Algebra

This part comprises the following chapters:

Chapter 1, Vectors and Vector Spaces
Chapter 2, The Geometric Structure of Vector Spaces
Chapter 3, Linear Algebra in Practice
Chapter 4, Linear Transformations
Chapter 5, Matrices and Equations
Chapter 6, Eigenvalues and Eignevectors
Chapter 7, Matrix Factorizations
Chapter 8, Matrices and Graphs

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Tech Concepts

36

Programming languages

73

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Mathematics of Machine Learning

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Mathematics of Machine Learning

Published in: May 2025

Publisher: Packt

ISBN-13: 9781837027873

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Authors (1)

Tivadar Danka

Tivadar Danka

Tivadar Danka is a mathematician by training, a machine learning engineer by profession, and an educator by passion. After finishing his PhD in 2016 (about the arcane subject of orthogonal polynomials), he switched career paths and has been working in machine learning ever since. His work includes applying deep learning to cell microscopy images to identify and phenotype cells, creating one of the most popular open source Python packages for active learning, building a full machine learning library from scratch, and collecting about a total of 100k followers on social media, all by posting high-quality educational content.

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