Linear Algebra for Machine Learning

Open the black box of machine learning.

Jumping into machine learning has never been easier. But libraries like TensorFlow and PyTorch hide the complexities from you. Looking under the hood is a superpower, and machine learning is written in the language of linear algebra.

This book is the best way to learn it.

Make matrices your most powerful tool. Master linear algebra through intuitive and clear explanations with a focus on machine learning.

Intuitive and clear explanations. Every concept is explained from the ground up, leading with intuition and examples. Mathematics does not have to be complicated.

Math, with machine learning in mind. Focusing on math that is core to machine learning. No distractions, no detours.

What you’ll get?

✅ A 230-page ebook in PDF and jupyter-book (HTML) format

✅ All the linear algebra you need to master machine learning

✅ Beginner-friendly explanations

✅ Python code examples for ALL the concepts

✅ 45 practice problems to enhance your understanding

✅ Free updates to the book forever

What you'll learn?

• How data is represented by vectors and matrices,
• What is the optimal way to represent vectors and matrices inside a computer,
• Why is matrix multiplication defined the way it is,
• How to work with vectors and matrices in practice,
• What is the geometric structure of vector spaces,
• Why are vectors and matrices the fundamental building block of machine learning,
• Why are linear transformations essential in machine learning and what do matrices have to do with them,
• What eigenvalues and eigenvectors are and why are they extremely useful in practice,
• Why the Singular Vector Decomposition is the pinnacle result of linear algebra,

...and many more!

Table of Contents

Linear algebra:

1. Vectors in theory
2. Vectors in practice
3. The geometric structure of vector spaces: measuring distances
4. Inner products, angles, and lots of reasons to care about them
5. The first steps in computational linear algebra
6. Matrices, the workourses of machine learning
7. Linear transformations
8. Determinants, or how linear transformations affect volume
9. Linear equations
10. The LU decomposition
11. Determinants in practice
12. Eigenvalues and eigenvectors
13. Special transformations and matrix decompositions
14. Computing eigenvalues

Appendix:

1. It's just logic
2. The structure of mathematics
3. Basics of set theory
4. Numbers
5. Complex numbers

Refund policy

If you find that the Linear Algebra for Machine Learning book is not for you, no worries! Let me know within 30 days of your purchase, and I'll refund you immediately - no questions asked.

Disclaimer

This book is the standalone linear algebra part from my Mathematics of Machine Learning book, currently out in early access. If you would like to upgrade your purchase to the Mathematics of Machine Learning early access, send me an email or a message through Twitter.