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Concept visual
Start from A
Neural Networks are used in applications like Facial Recognition.
Pattern Recognition.
Perceptron. Perceptrons can be used to classify data into two parts.
Linear Binary Classifiers.
Imagine a strait line (a linear graph) in a space with scattered x y points. How can you classify the points over and under the line? A perceptron can be trained to recognize the points over the line, without knowing the formula for the line.
To program a perceptron, we can use a simple JavaScript program that will:
Creating a simple plotter object is described in the AI Canvas Chapter.
const plotter = new XYPlotter("myCanvas");
plotter.transformXY();
const xMax = plotter.xMax;
const yMax = plotter.yMax;
const xMin = plotter.xMin;
const yMin = plotter.yMin;Create as many xy points as wanted. Let the x values be random (between 0 and maximum). Let the y values be random (between 0 and maximum). Display the points in the plotter:
const numPoints = 500;
const xPoints = [];
const yPoints = [];
for (let i = 0; i < numPoints; i++) {
xPoints[i] = Math.random() * xMax;
yPoints[i] = Math.random() * yMax;
}Display the line in the plotter:
Example function f(x) {
return x * 1.2 + 50;
}Compute the correct answers based on the line function:
Formula
y = x * 1.2 + 50.The desired answer is 1 if y is over the line and 0 if y is under the line. Store the desired answers in an array (desired[]).
let desired = [];
for (let i = 0; i < numPoints; i++) {
desired[i] = 0;
if (yPoints[i] > f(xPoints[i])) {desired[i] = 1;}
}For each point, if desired[i] = 1 display a black point, else display a blue point.Example for (let i = 0; i < numPoints; i++) {
let color = "blue";
if (desired[i]) color = "black";
plotter.plotPoint(xPoints[i], yPoints[i], color);
}In the next chapter, you will learn how to use the correct answers to: Train a perceptron to predict the output values of unknown input values.