Softmax

Takes a whole vector $Z\in {\mathbb{R}}^n$ and generates as output a vector $A\in [0,1]$ with the property that ${\sum }_{i=1}^n{A}_i=1$, which means we can interpret it as a probability distribution over $n$ items:

$$\text{softmax}(z)=\left[\begin{array}{c}\exp (z_1)/{\sum }_i\exp (z_i)\\ ⋮\\ \exp (z_n)/{\sum }_i\exp (z_i)\end{array}\right]$$

Softmax is similar to sigmoid in concept (used for outputting probabilities/confidences) but in higher dimensions. Commonly used for multi-class classification.