What do you see when you look at this image? Well, it's a cute dog playing with a ball

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What do you see when you look at this image? Well, it's a cute dog playing with a ball. Easy, right? This seemingly obvious observation is not an easy task for a computer. How does a computer that processes everything in 0s and 1s even store this image? The answer is matrices!

‍ A digital image is made up of small indivisible units called pixels. Each pixel of an image is represented by an intensity value. Therefore, an image is essentially a matrix whose elements are the intensity values of each individual pixel. ‍

To expand, compress, crop or perform any operation on these images, linear algebra is most likely involved. So what is linear algebra?

Linear algebra is a branch of mathematics that deals with linear equations and linear functions which are represented through matrices and vectors. In simpler words, linear algebra helps you understand geometric concepts such as planes, in higher dimensions, and perform mathematical operations on them. It can be thought of as an extension of algebra into an arbitrary number of dimensions. Rather than working with scalars, it works with matrices and vectors.

Why do I need to study linear algebra for machine learning?

Linear algebra is the building block of machine learning and deep learning. Understanding these concepts at the vector and matrix level deepens your understanding and widens your perspective of a particular ML problem.

Here are a few applications of linear algebra in machine learning:

It can’t be emphasized enough how fundamental linear algebra is to machine learning. For those of you that want a deeper understanding of the inner workings of machine learning and deep learning algorithms, linear algebra is essential.

Okay, I guess I do need it. Where do I begin?

Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. Khan Academy's linear algebra lecture series provides a thorough introduction to linear algebra from the basics of vectors and matrices to projections into lower-dimensional spaces, eigenvectors, and eigenvalues. If you are new to all of these concepts, this is a good place to start!

The Essence of linear algebra playlist contains 14 video lectures by Grant Sanderson. The lectures give a geometric understanding of the subject with good visualizations. The lectures cover vectors, linear combinations, matrices, determinants, inverse matrices, systems of linear equations, dot products, cross products, transformations, eigenvalues, and eigenvectors. If you are a visual learner, these videos are for you! If you are already familiar with the fundamentals of linear algebra, these videos can help you brush up on your basics.

This course parallels the combination of theory and applications in Professor Strang’s textbook Introduction to Linear Algebra. Dr. Gilbert Strang picks out four key applications in the book: Graphs and Networks; Systems of Differential Equations; Least Squares and Projections; and Fourier Series and the Fast Fourier Transform.

This leading textbook Introduction to Linear Algebra gives a clear introduction to the subject of linear algebra. Unlike most other linear algebra textbooks, the approach is not a repetitive drill and shows the beauty and variety of the subject. The self-teaching book is loaded with examples and graphics and provides a wide array of probing problems, accompanying solutions.

Strang's Linear Algebra and Its Applications contains a wealth of applications and examples of how to use linear algebra in science and engineering. This is a great reference book for anyone who has already taken an introductory course in linear algebra.

This text combines the many simple and elegant results of elementary linear algebra with some powerful computational techniques to demonstrate that theoretical mathematics need not be difficult or mysterious. This book is written for the second course in linear algebra.

Hope you got learn the importance of linear algebra for machine learning. Like this article? Learn about other machine learning prerequisites - probability, calculus, and Git and Anaconda.