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Mathematics of Machine Learning

Mathematics of Machine Learning

By : Tivadar Danka
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Mathematics of Machine Learning

Mathematics of Machine Learning

By: Tivadar Danka

Overview of this book

Mathematics of Machine Learning provides a rigorous yet accessible introduction to the mathematical underpinnings of machine learning, designed for engineers, developers, and data scientists ready to elevate their technical expertise. With this book, you’ll explore the core disciplines of linear algebra, calculus, and probability theory essential for mastering advanced machine learning concepts. PhD mathematician turned ML engineer Tivadar Danka—known for his intuitive teaching style that has attracted 100k+ followers—guides you through complex concepts with clarity, providing the structured guidance you need to deepen your theoretical knowledge and enhance your ability to solve complex machine learning problems. Balancing theory with application, this book offers clear explanations of mathematical constructs and their direct relevance to machine learning tasks. Through practical Python examples, you’ll learn to implement and use these ideas in real-world scenarios, such as training machine learning models with gradient descent or working with vectors, matrices, and tensors. By the end of this book, you’ll have gained the confidence to engage with advanced machine learning literature and tailor algorithms to meet specific project requirements. *Email sign-up and proof of purchase required
Table of Contents (36 chapters)
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2
Part 1: Linear Algebra
11
References
12
Part 2: Calculus
19
References
20
Part 3: Multivariable Calculus
24
References
25
Part 4: Probability Theory
29
References
30
Part 5: Appendix
31
Other Books You May Enjoy
32
Index

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.

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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Mathematics of Machine Learning
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