The study of Mathematics has come a long way. From the discovery of zero to solving complex integral equations which somewhere surely has impacted the lives of many. Well, not many realise it but if maths had not been there, we wouldn’t have been able to sit in front of our laptops, these beautiful architectural monuments we see today wouldn’t have existed, we would never have been able to reach the moon or discover more about the black hole etc.

Realising the importance of maths and how it is valued among the researchers of tech giants for the purpose of Machine Learning or Computation, I will take you to a journey while discussing a really basic yet an important aspect of mathematics that is used in Machine Learning, vectors.

Overview

1. Physical Quantities.
2. Vector and Scalars
3. Representation of vectors.
4. Types of Vectors
5. Operations on Vectors
6. Section Formula
7. Concept of Euclidean Distance between two vectors.
8. Where are vectors used in Machine Learning

What are Physical Quantities

Any quantity that can be measured in terms of numbers is called a physical quantity. Measurements in terms of size, mass etc. Let us understand it better with an example, your mom has asked you to buy 2 Kilograms of tomatoes, you convey the amount you want to buy to the vendor. He “measures” the mass and then gives you the desired quantity of tomatoes. Here since the mass can be measured in terms of numbers, it will be called a physical quantity.

Let’s delve a little more into it.

Physical quantities can be categorized into 2 categories: Scalars and Vectors…

#developers corner #linear algebra #machine learning #data-science