What is an activation function used for?#
By acting as a gatekeeper for information, an activation function allows the model to learn complex patterns and make more accurate decisions, improving the reliability of its predictions and analysis.
How does an activation function work?#
An AI model is made up of many interconnected digital "neurons." Each neuron receives data from other neurons in the form of numerical signals. It adds these signals together to get a total score.
The activation function then takes this score and decides what the neuron's output should be. The common ones are not on/off switches but smooth or piecewise curves: ReLU, the most widely used, passes a positive score straight through and outputs zero for anything negative, while sigmoid and tanh squash any score into a fixed range. What matters is that the response is not a straight line. Stack only straight-line steps and the whole network collapses into one straight-line step, no matter how many layers it has; the bend the activation function introduces is what lets the network represent complex patterns. It also has to be smooth enough to be differentiable, because training works by measuring how the output changes as each weight changes, which a true on/off switch would not allow.