How does a machine actually “learn” from its mistakes? Imagine a simple robot, staring at data, making a guess—and then, when it gets the answer wrong, tweaking a knob until it gets it right. That’s the heart of the Perceptron Learning Algorithm: an intuitive loop of trial, error, and gradual self-improvement.
As in our last blog on Perceptron (https://akashdascodes.com/what-is-a-perceptron/), we discussed what it means to train a perceptron—which essentially involves finding a line (the decision boundary) that separates sample data into categories, aiming to minimize classification error. Now, the important question is: How do you find the linearly separable line?
For that, we will follow a number of steps:
Step 1: Take a random decision boundary
Take a random value of a, b, c to create a random line: \[
a x_{1} + b x_{2} + c > 0
\]