Activation functions are an essential component of artificial neural networks, providing non-linearities that allow models to learn complex patterns from data. Below, we explore some of the most commonly used activation functions in neural networks.

Types of Activation Functions

  1. Sigmoid

    • The sigmoid function maps any real-valued number into the (0, 1) interval.
    • It is useful for binary classification problems.

  2. ReLU (Rectified Linear Unit)

    • The ReLU function is defined as f(x) = max(0, x).
    • It is highly popular due to its simplicity and effectiveness in deep networks.

  3. Tanh (Hyperbolic Tangent)

    • The tanh function maps any real-valued number into the (-1, 1) interval.
    • It is similar to the sigmoid function but with a wider range.

  4. Softmax

    • The softmax function is used in multi-class classification problems.
    • It converts a vector of real values into a probability distribution.

Resources

For more information on activation functions and their applications, you can visit our Neural Networks Guide.