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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
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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

11.3 Continuity

If I asked you to conjure up a random function from your mind, I am almost certain that you would come up with one that is both continuous and differentiable. (unless you have weird tastes, as many mathematicians do).

However, the vast majority of functions are neither. In terms of cardinality, if you count all real functions f : , it turns out that there are 2c of them in total, but the subset of continuous ones have cardinality c. It is hard to imagine such quantities: c and 2c are both infinite, but, well, 2c is more infinite. Yeah, I know. Set theory is weird. (Recall that c denotes the cardinality of the set of real numbers. If you would like a refresher on the topic, check out the set theory appendix Appendix C.)

Overall, as we shall see, continuity and differentiability allow us to do meaningful work with functions. For instance, the usual gradient descent-based optimization for neural networks doesn’t work if the loss function and...

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