It is an algorithm used for supervised machine learning. It is the building block of Deep Learning. It is a mathematical model.
W1, W2 are the weights and b here refer to the bias
Weights tell us about the feature importance.
bias is provided for increasing model accuracy.
X1 and X2 here are the input features and f is the activation function
Z = W1X1 + W2X2 + b
The summation function Z is passed to the activation function.
The work of the activation is to bring the Z in a range of [-1,1] or [0,1], etc.
Suppose there is a perceptron with a step function as the activation function
Here z = W1X1 + W2X2 + b
Let W1 = A, W2 = B, b = C
X1 = x, X2 = y
So the equation goes by,
Ax + By + C which is a equation of line if we equate with
And interpret with
Ax + By + C >= 0 and
Ax + By + C < = 0
It creates a line in 2D graph and provides the region for classification in it just like,
Perceptron is nothing but a line to create region.
In 3-dimensional feature perceptron becomes a plane.
Perceptron is used in linear or sort of linear
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