About This Book
The purpose of this book is to teach you modern linear algebra. But this is not about memorizing some key equations and slugging through abstract proofs; the purpose is to teach you how to think about matrices, vectors, and operations acting upon them. You will develop a geometric intuition for why linear algebra is the way it is. And you will understand how to implement linear algebra concepts in Python code, with a focus on applications in machine learning and data science.
Many traditional linear algebra textbooks avoid numerical examples in the interest of generalizations, expect you to derive difficult proofs on your own, and teach myriad concepts that have little or no relevance to application or implementation in computers. I do not write these as criticisms—abstract linear algebra is beautiful and elegant. But if your goal is to use linear algebra (and mathematics more generally) as a tool for understanding data, statistics, deep learning, image processing, etc., then traditional linear algebra textbooks may seem like a frustrating waste of time that leave you confused and concerned about your potential in a technical field.
This book is written with self-studying learners in mind. Perhaps you have a degree in math, engineering, or physics, but need to learn how to implement linear algebra in code. Or perhaps you didn’t study math at university and now realize the importance of linear algebra for your studies or work. Either way, this book is a self-contained resource; it is not solely a supplement for a lecture-based course (though it could be used for that purpose).
If you were nodding your head in agreement while reading the past three paragraphs, then this book is definitely for you.
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