Interactive visualizations of common activation functions used in deep learning
Activation functions are mathematical equations that determine the output of a neural network node. They introduce non-linearity into the network, allowing it to learn complex patterns and relationships in data.
Use the sliders above to input different values (x) and see how each activation function transforms that input into an output. The graphs show the full range of the function, while the output display shows the exact result for your selected input.