Activation Functions

Activation functions are mathematical equations that determine the output of a neural network node or neuron. They introduce non-linearity into the model, allowing it to learn complex patterns in the data. Common activation functions include sigmoid, ReLU (Rectified Linear Unit), and tanh, each with distinct characteristics that influence the training and performance of the model. They are crucial in deep learning architectures for tasks such as image recognition, language processing, and more, enabling the network to make decisions based on the input data.