Activation Functions in Neural Networks: Types, Role, Full Guide

The most common activation functions include ReLU (Rectified Linear Unit), sigmoid, tanh (hyperbolic tangent), and softmax (used in the output layer for multi-class classification).

ReLU (Rectified Linear Unit) is a widely used activation function that outputs the input directly if it's positive; otherwise, it outputs zero. Its role is to introduce non-linearity and help mitigate the vanishing gradient problem, making deep networks easier to train.

The sigmoid activation function is useful for binary classification problems where the network needs to output probabilities between 0 and 1. It's commonly used in the output layer for such tasks.

The tanh (hyperbolic tangent) activation function is similar to sigmoid but outputs values between -1 and 1. It's useful for tasks where the data is centered around zero and can help in learning more efficiently in deep networks.

The choice of activation function depends on the nature of your task, the properties of your data, and the architecture of your neural network. Experimentation and considering factors like vanishing gradients and non-linearity are crucial.

While ReLU is popular, it can suffer from the "dying ReLU" problem, where neurons can become inactive. Leaky ReLU and Parametric ReLU (PReLU) are variants that address this issue.

Softmax is used in the output layer for multi-class classification. It converts raw scores (logits) into probabilities, where each class gets a probability score, and the sum of all probabilities equals 1.

Yes, it's common to use different activation functions in different layers of a neural network, depending on the requirements of each layer and the problem being solved.

Recent research has led to the development of novel activation functions like Swish and GELU, which aim to improve the performance of deep neural networks.