Deep Learning Tutorial

It is an algorithm used for supervised machine learning. It is the building block of Deep Learning. It is a mathematical model.

W_{1}, W_{2} are the weights and b here refer to the bias

Weights tell us about the feature importance.

bias is provided for increasing model accuracy.

X_{1} and X_{2} here are the input features and f is the activation function

Z = W_{1}X_{1} + W_{2}X_{2} + 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 = W_{1}X_{1} + W_{2}X_{2} + b

Let W_{1} = A, W2 = B, b = C

X_{1} = x, X_{2} = 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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