• When you take a digital photo with your phone or transform  the image in photoshop,
  • when you play a video game or watch a movie with digital effects,
  • when you do a web search or make a phone call
you are using technologies that build upon linear algebra.

Linear algebra is built upon two basic elements: the matrix and the vector!

Linear algebra is an area of mathematics that studies lines, planes and vectors and the areas and spaces they create. Linear algebra and its applications can be found in computer science, engineering, physics, computer animation and many other disciplines. Linear algebra is vital to many areas of computer science because linear equations are so easy to solve.  It converts a large number of problems to matrix.

Hence, we solve the matrix!(A---------------------------------------------------

What do you see when you look at the image below? You most likely said flower, leaves -not too difficult. But, if I ask you to write that logic so that a computer can do the same for you – it will be a very difficult task (to say the least).

You were able to identify the flower because the human brain has gone through million years of evolution. We do not understand what goes in the background to be able to tell whether the colour in the picture is red or black. We have somehow trained our brains to automatically perform this task.

But making a computer do the same task is not an easy task, and is an active area of research in Machine Learning and Computer Science in general. But before we work on identifying attributes in an image, let us ponder over a particular question- How does a machine stores this image?

You probably know that computers of today are designed to process only 0 and 1. So how can an image such as above with multiple attributes like colour be stored in a computer? This is achieved by storing the pixel intensities in a construct called Matrix. Then, this matrix can be processed to identify colours etc.

So any operation which you want to perform on this image would likely use Linear Algebra and matrices at the back end. (A comprehensive beginners guide to Linear Algebra for Data Scientists - Analytics Vidhya)