Softmax: Why neural networks need non-linearity? life isn't straight-line simple

Math functions that calculate weighted sum of inputs and adds bias to give non-linearity to output of neuron.

What is an Activation Function?

An activation (or transfer) function maps a neuron’s weighted inputs plus bias to its output, adding non-linearity so the model can learn complex patterns beyond simple linear ones.

Activation Functions are also known as Transfer Function in the context of Neural Networks.

Math functions that calculate weighted sum of inputs and adds bias to give non-linearity to output of neuron.

Decides whether a neuron should be activated (“fired”) or not.

This helps Neural Network to use important information and suppress not so useful data points.

Adds non-linearity to Neural Network to tackle complex problems.

Real-world problems are non-linear. Recognizing cats vs. dogs

Without activation functions, f(z) = z , linear regression model, multiple linear layers form up to one big linear equation; useless for non-linear problems.

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