Activation Functions

Activation functions are mathematical functions used in artificial neural networks that help determine the output of a neuron. They take the input signals (or weighted sums from previous neurons) and transform them into an output, introducing non-linearities. This is crucial because it allows neural networks to learn complex patterns in data. Common activation functions include the sigmoid, which squashes values between 0 and 1, and the ReLU (Rectified Linear Unit), which outputs zero for negative inputs and the raw input for positive ones. By enabling complex decision-making, activation functions play a key role in the performance of neural networks.