Sigmoid
Squashes any real number into the range zero to one, so the output can be read as a probability. Its gradient vanishes at both ends, which is exactly why deep networks moved to ReLU for hidden layers and kept sigmoid for the output.
- The input, an unbounded real number.
- The exponential that produces the S-shape.