Explaining perceptron to a 6 year old!!
What is perceptron ?
A perceptron is like a little robot that helps us classify things into different groups. Imagine you have a basket of apples and bananas. The perceptron can help us sort them into two piles, one for apples and one for bananas. The way it does this is by looking at each piece of fruit and deciding which pile it should go in based on certain traits, like its shape and color. The more the perceptron sorts fruit, the better it gets at knowing which traits to look for and making the right decision.
How does it work ?
Okay, let’s pretend we want the perceptron to help us sort fruits into two piles: apples and bananas.
The first step is to give the perceptron some rules to help it make decisions. For example, if a fruit is round, it’s a apple, and if it’s elongated, it’s an banana.
Next, the perceptron looks at each fruit and uses these rules to decide which pile the fruit should go in. If the fruit is round, the perceptron will place it in the bananas pile, and if it’s elongated, the perceptron will place it in the apples pile.
But what if the perceptron makes a mistake and puts a banana in the apples pile? That’s okay! The perceptron is designed to learn from its mistakes. We can tell it which decision was incorrect and adjust its rules so it’s less likely to make the same mistake again in the future.
The more fruits the perceptron sorts, the better it becomes at classifying them correctly. And that’s how a perceptron works!
Problem with perceptron ?
The linearity problem with perceptron refers to its limitations in solving certain kinds of problems. Imagine you have a big bag of mixed fruit and you want to sort it into two piles: apples and bananas. But this time, the fruit is mixed in such a way that you can’t use simple rules like shape or color to sort them. For example, maybe some of the bananas are yellow and some of the apples are green.
A perceptron might struggle with this kind of problem because it can only use straight lines to separate the fruit into two piles. But in this case, the lines between the apples and bananas are jagged and not straight, so a perceptron wouldn’t be able to accurately sort the fruit.
This is the linearity problem with perceptron — it can only solve problems where the lines between the categories are straight. But don’t worry, there are other types of machine learning algorithms that can solve more complex problems.
