📘 Lesson  ·  Lesson 90

Activation Functions

What is an Activation Function?

💡 At a Glance

An activation function decides whether a neuron "fires". It adds non-linearity so neural networks can learn complex patterns.

Common Functions

FunctionOutput RangeUse
Sigmoid0 to 1binary output / probabilities
Tanh-1 to 1hidden layers (zero-centered)
ReLU0 to infinitymost common in deep networks

In Code

Python
import numpy as np
def relu(x): return np.maximum(0, x)
def sigmoid(x): return 1 / (1 + np.exp(-x))

print(relu(-3), relu(5))      # 0 5
print(round(sigmoid(0), 2))   # 0.5
0 5 0.5

Summary

  • Activation functions add non-linearity to neural networks.
  • ReLU is most common; Sigmoid for probabilities; Tanh is zero-centered.
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