Activation functions are essential components of neural networks, allowing them to process complex data by introducing non-linearity into the model, thus enabling the networks to learn intricate patterns and relationships. These functions, akin to neurons in the human brain, determine the output of a node based on specific inputs and are crucial for the effective functioning of machine learning, deep neural networks, and artificial intelligence models. Various activation functions, such as Sigmoid, Tanh, and Rectified Linear Unit (ReLU), each have unique properties and applications, with the choice of function often depending on the specific task and data involved. Without activation functions, neural networks would function merely as linear regression models, limiting their ability to handle complex datasets. The architecture of neural networks comprises input, hidden, and output layers, where activation functions play a key role in processing and optimizing data flow, thereby enhancing model performance and output accuracy. The selection of an appropriate activation function often involves trial and error, guided by the problem type and the architecture of the neural network, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs).