Activation Functions in Deep Learning

Activation functions are a crucial component of artificial neural networks, providing non-linearities that allow models to learn complex patterns from data. Here's a brief overview of some commonly used activation functions in deep learning:

Sigmoid: The sigmoid function maps any real-valued number into the (0, 1) interval. It is often used in binary classification problems.
ReLU (Rectified Linear Unit): The ReLU function is defined as f(x) = max(0, x). It is computationally efficient and helps mitigate the vanishing gradient problem.
Tanh (Hyperbolic Tangent): The tanh function is similar to the sigmoid function but maps the input to the (-1, 1) interval. It is often used in deep networks as it helps with the vanishing gradient problem.
Leaky ReLU: Leaky ReLU is a variant of ReLU that allows a small gradient when the input is negative, preventing dead neurons.

For more information on activation functions, you can read our comprehensive guide on Deep Learning Activation Functions.