In Depth
Activation functions are a fundamental component of neural networks that determine whether and how strongly a neuron 'fires' given its inputs. Without activation functions, a neural network would be limited to learning linear relationships regardless of depth, because stacking linear transformations always produces another linear transformation. Activation functions introduce the non-linearity that gives deep networks their expressive power.
Common activation functions include ReLU (Rectified Linear Unit, which outputs zero for negative inputs and passes positive inputs through), GELU (Gaussian Error Linear Unit, used in transformers), sigmoid (squashing values between 0 and 1), tanh (squashing between -1 and 1), and SiLU/Swish (a smooth approximation of ReLU). The choice of activation function affects training dynamics, model performance, and computational efficiency.
ReLU revolutionized deep learning by solving the vanishing gradient problem that plagued sigmoid and tanh activations in deep networks. GELU has become the default in transformer architectures due to its smooth gradient properties. The development of new activation functions continues to be an active research area, with recent work on hardware-efficient alternatives and learned activation functions that adapt during training.