Activation functions play a crucial role in deep learning by introducing non-linearity into neural networks, enabling them to learn complex patterns. Here are some of the most commonly used activation functions:

Sigmoid: This function maps input values between 0 and 1, making it useful for binary classification. However, it suffers from the vanishing gradient problem, which slows down training.

ReLU (Rectified Linear Unit): ReLU replaces negative values with zero while keeping positive values unchanged. It is computationally efficient and widely used in hidden layers of deep networks. However, it has a drawback known as the "dying ReLU" problem, where some neurons become inactive.

Leaky ReLU: To overcome the dying ReLU problem, Leaky ReLU allows a small negative slope for negative input values, preventing neurons from becoming completely inactive.

Tanh (Hyperbolic Tangent): Similar to Sigmoid but ranges between -1 and 1, Tanh is often used in recurrent neural networks (RNNs) due to its ability to center the data around zero. However, it still faces the vanishing gradient issue.

Softmax: Typically used in the output layer of classification models, Softmax converts raw scores into probabilities, making it ideal for multi-class classification tasks.

Swish: Introduced by Google, Swish is a smooth, non-monotonic function that outperforms ReLU in certain deep learning applications by allowing small negative values to pass through.

Choosing the right activation function depends on the problem being solved, the depth of the network, and computational constraints. Understanding these functions is essential for optimizing deep learning models. If you want to master these concepts, consider enrolling in a data science and machine learning course to gain hands-on experience.