Softmax vs Sigmoid Functions

Two of the most common activation functions… and two of the most misunderstood.

Sigmoid: squashes input into a range between 0 and 1. Perfect for binary classification (yes/no problems). Example: spam or not spam.

Softmax: takes a vector of numbers and turns them into probabilities that sum to 1. Perfect for multi-class classification (cat vs dog vs horse).

👉 Rule of thumb:

Binary task → use Sigmoid.
Multi-class task → use Softmax.

Simple, but if you get this wrong, your model will never make sense.