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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

How to read this book

Mathematics follows a definition-theorem-proof structure that might be difficult to follow at first. If you are unfamiliar with such a flow, don’t worry. I’ll give a gentle introduction right now.

In essence, mathematics is the study of abstract objects (such as functions) through their fundamental properties. Instead of empirical observations, mathematics is based on logic, making it universal. If we want to use the powerful tool of logic, the mathematical objects need to be precisely defined. Definitions are presented in boxes like this below.

Definition 1. (An example definition)

Definitions appear like this.

Given a definition, results are formulated as if A, then B statements, where A is the premise, and B is the conclusion. Such results are called theorems. For instance, if a function is differentiable, then it is also continuous. If a function is convex, then it has global minima. If we have a function, then we can approximate it with arbitrary precision using a single-layer neural network. You get the pattern. Theorems are the core of mathematics.

We must provide a sound logical argument to accept the validity of a proposition, one that deduces the conclusion from the premise. This is called a proof, responsible for the steep learning curve of mathematics. Contrary to other scientific disciplines, proofs in mathematics are indisputable statements, set in stone forever. On a practical note, look out for these boxes.

Theorem 1. (An example theorem)

Let x be a fancy mathematical object. The following two statements hold.

(a If A, then B.

(b) If C and D, then E.

Proof. This is where the proof goes.

To enhance the learning experience, I’ll often make good-to-know but not absolutely essential information into remarks.

Remark 1. (An exciting remark)

Mathematics is awesome. You’ll be a better engineer because of it.

The most effective way of learning is building things and putting theory into practice. In mathematics, this is the only way to learn. What this means is that you need to read through the text carefully. Don’t take anything for granted just because it is written down. Think through every sentence. Take every argument and calculation apart. Try to prove theorems by yourself before reading the proofs.

With that in mind, let’s get to it! Buckle up for the ride; the road is long and full of twists and turns.

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