Linear Algebra is a subject that you will find common in any field of science, engineering and mathematics whether it be Computer Science, Statistics, Physics.
Even though, the name suggests, its a linear subject but there is a lot of non-linear concepts in it as well.
Haha, dont worry. I will explain it in simple terms for you and along with its history and practical applications and its calculations as well.
Linear Algebra's foundational concepts was developed by Mathematician Hermann Grassmann in 1844 in his book "Theory Of Extension".
But the linear algebra we study today is not limited to what Grassmann introduced in 1844. There were many other mathematicians who contributed to the development of Linear Algebra, which we study today.
Some of those Giga Brains are James Joseph Sylvester (introduced Matrix)
This article can not include all the application of the Linear Algebra, because its used in almost every field of science, engineering and mathematics.
But some of the common applications are: Prediction, Cryptography, Image Processing, Machine Learning, Robotics, Quantum Computing etc.
In Machine Learning, Linear Algebra is used to calculate the weight of the model. As the model have lots of features, each feature is assigned a weight. Those weights are arranged in a structure called Matrix (which is a collection of vectors).
The model adjusts the weights of the model with the help of derivative of a loss function.
Algebra and Linear Algebra sound very similar but they are very different from each other. Algebra is a generalization of arithmetic, where the letters are used to represent the unknown values, and operate on them.
However, Linear Algebra is a branch of algebra that deals with the study of vectors, spaces, etc. It is a advanced form of algebra where the operation are performed on the vectors and matrices.
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