Explain the role of activation functions in neural networks and provide examples of commonly used activation functions.

Activation functions play a crucial role in neural networks by introducing non-linearity to the network's computations. They determine the output of a neuron based on the weighted sum of its inputs. Activation functions help neural networks learn complex patterns and make predictions by introducing non-linear relationships between inputs and outputs. The main purpose of an activation function is to introduce non-linearity, allowing the neural network to model and approximate non-linear functions effectively. Without activation functions, neural networks would be limited to representing only linear functions, severely restricting their ability to learn and solve complex problems. There are several commonly used activation functions in neural networks, each with its own characteristics and applicability in different scenarios. Here are some examples: 1. Sigmoid Activation Function: The sigmoid function is a popu....