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Linear Algebra In Machine Learning

Linear Algebra is a part of mathematics that deals with linear equations and linear functions which are represented through matrices and vectors.
Linear Algebra In Machine Learning

Linear Algebra is a part 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.

Now what do you see below, well it is a cute dog playing with the ball. Easy right? This easy observation is not an easy task for computer. A question arises that How does a computer which stores everything in 0 and 1 even stores this image?

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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.

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.

Why studying Linear Algebra is important for Machine Learning and where to start
https://www.univ.ai/post/linear-algebra-machine-learning-prerequisites

#Mathematics #LinearAlgebra #MachineLearning #Data #DataScience #Probyto #ProbytoAI

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