What is an Activation Function? A Complete Guide.

Activation functions are integral to the functionality of neural networks in deep learning, playing a crucial role in tasks like image classification and language translation by determining the accuracy and convergence speed of model outputs. These functions decide whether a neuron should be activated based on its input, transforming the summed weighted input into an output for subsequent layers or final results. Non-linear activation functions, such as ReLU, sigmoid, tanh, and Softmax, are essential for enabling neural networks to learn complex patterns, offering advantages over linear functions by allowing backpropagation and the stacking of multiple layers. Each activation function has its benefits and drawbacks; for example, ReLU is computationally efficient but struggles with negative inputs, while Softmax is ideal for multi-class classification problems. The choice of activation function is dictated by the architecture of the neural network and the type of prediction problem, with guidelines suggesting ReLU-based functions for hidden layers, sigmoid for binary classification, and Softmax for multi-class classification.