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Linear algebra textbooks will teach you linear algebra in the classical university bottom-up approach.This is too slow (and painful) for your needs as a machine learning practitioner.In this new laser-focused Ebook written in the friendly Machine Learning Mastery style that you’re used to, you will finally cut through the equations, Greek letters, and confusion, and discover the topics in linear algebra that you need to know.Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover what linear algebra is, the importance of linear algebra to machine learning, vector, and matrix operations, matrix factorization, principal component analysis, and much more.Linear algebra is the mathematics of data and the notation allows you to describe operations on data precisely with specific operators.You need to be able to read and write this notation.

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Only a specific subset of linear algebra is required, though you can always go deeper once you have the basics.As a first step, think of linear algebra as a shortcut language or notation to make describing some operations compact.There are common machine learning techniques that can only be understood via their linear algebra descriptions because they come from the field.You’re reading along, things are going well, and then you hit an equation, and you are stopped in your tracks with questions like: Unless you have a basic knowledge of linear algebra, you will not be able to read and understand even the most basic equations.

Have you heard of Tensor Flow, Google’s Python library for deep learning?

A better fit for developers is to start with systematic procedures that get results, and work back to the deeper understanding of theory, using working results as a context.

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