Activation Functions
An “activation function” is a crucial component of a neural network that helps introduce non-linearity and enables the network to learn complex patterns and relationships in data. Think of it as a mathematical function that takes the weighted sum of inputs from the previous layer and applies a transformation to produce an output for each neuron. It acts as a decision-maker, determining whether the neuron should be activated or not based on the input it receives. Activation functions like the sigmoid function or the rectified linear unit (ReLU) function are commonly used. By applying these functions, the network becomes capable of learning and modeling complicated relationships in the data, enhancing its ability to solve complex problems and make accurate predictions.
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