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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

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Product type Paperback
Published in May 2025
Publisher Packt
ISBN-13 9781837027873
Length 730 pages
Edition 1st Edition
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Author (1):
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Tivadar Danka Tivadar Danka
Author Profile Icon Tivadar Danka
Tivadar Danka
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Toc

Table of Contents (36) Chapters Close

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

5.1 Linear equations

In practice, we can translate several problems into linear equations. For example, a cash dispenser has $900 in $20 and $50 bills. We know that there are twice as many $20 bills than $50. The question is, how many of each bill does the machine have?

If we denote the number of $20 bills by x1 and the number of $50 bills by x2, we obtain the equations

 x1 − 2x2 = 0 20x1 + 50x2 = 900.

For two variables, as we have now, these are easily solvable by expressing one in terms of the other. Here, the first equation would imply x1 = 2x2. Plugging it back into the second equation, we obtain 90x2 = 900, which gives x2 = 10. Coming full circle, we can substitute this into x1 = 2x2, yielding the solutions

x = 20 1 x2 = 10.

However, for thousands of variables like in real applications, we need a bit more craft. This is where linear algebra comes in. By introducing the matrix and vectors

 ⌊ ⌋ ⌊ ⌋ ⌊ ⌋ A = ⌈ 1 − 2⌉ , x = ⌈x1 ⌉, b = ⌈ 0 ⌉ , 20 50 x2 900

the equation can be written in the form Ax = b. That is, in terms of linear transformations, we can reformulate the question: which vector...

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