An introduction to vectorization - by Tivadar Danka
Yes, I know. You are more than familiar with linear regression; we are not here to discuss that. I want to share a wonderful mathematical principle with you, learning through the example of linear regression.
Depending on what we understand by a, x, b, +, and ·, the expression “ ax + b” can either be the very first machine learning model a student encounters or the main component of a powerful neural network.
This dance of generalization and abstraction is the essence of mathematics; it’s why we can treat functions as vectors, use matrices as exponents, build the foundations of mathematics by drawing dots and arrows, and many more.
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The fundamental problem of machine learning: predicting one variable from another. Mathematically speaking, we are looking for a function y = h( x) that describes the relation between the predictor variable x and the target variable y.
