Channel: Medium Scheduled Publishing date: Jan 13, 2019 Scheduled Writing: Jan 09, 2019 9:00 PM Tags: Medium
Machine learning seemingly at its core can be defined as a massive operation on the concepts of vectors, This is strictly my definition, my way of making this world of ours small and easy to understand. Vectors are the ways in which we represent data in machine learning, and linear algebra, the core mathematical concept behind the bulk of ML. For many people math is not fun and seemingly can never be that's why I decided to take a unqiue approach to it, not what we would consider textbook approach. So lets dive in one easy step at a time.
Many of us already know vectors, were were told about them while in school, those little guys, who seem to have it all magnitude and direction, but this never really explained them, why? because it was a one angle view. you see.
The Mathematician, The Physicist and the Data analyst see vectors differently, and it balances out in the Mathematician's point of view.
So how do they really see it.?
In physics vectors are usually represented with an arrow, in the data scientist's point of view, its a list of numbers, but in the mathematician's point of view it balances.
See with the cartesian plan vectors are really just arrow points from the origin which can be represented with paths from the x and y axis.
And that's what a vector is in representation because they are able to define any points in space, 2D, 3D etc, i.e with reference to an origin on the plain of sorts. To a data analyst this is key because it means they can defined data how they see fit.
Interestingly in the world of linear algebra, math in general and physics, vectors only really do two things.
- Being added together
- And scaling or expanding.
Vector Addition
The concept of adding vectors was usually about counter intuitive to me, but it always did come together. So lets look at it in a cooler way.
Let's consider two vectors on the plane,
adding this two vectors can simply be viewed as