person Author: Process Fellows
An activation function is a function applied to the weighted combination of all inputs to a neuron. Activation functions allow neural networks to learn complicated features in the data. They are typically of a non-linear nature.
Examples:

• Sigmoid function: Maps values to a range between 0 and 1. It is often used for binary classification.
• ReLU (Rectified Linear Unit): Outputs 0 for negative inputs and the input itself for positive inputs. It is widely used in hidden layers.
• Tanh function: Maps values to a range between -1 and 1. It is similar to sigmoid but centered around zero.
• Leaky ReLU: A variation of ReLU that allows small negative values instead of setting them completely to 0.
• Softmax function: Converts raw output values into probabilities. It is commonly used in the output layer for multi-class classification.