Activation functions are a crucial component of artificial neural networks, providing the non-linearities that allow models to learn complex patterns in data. In this exploration, we delve into some of the most commonly used activation functions.

Common Activation Functions

  1. Sigmoid 📈

    • The sigmoid function maps any real-valued number into the range (0, 1), making it useful for binary classification tasks.
    • It has a smooth, S-shaped curve and is defined as: [ \sigma(x) = \frac{1}{1 + e^{-x}} ]

  2. ReLU 🚀

    • The Rectified Linear Unit (ReLU) function is defined as: [ f(x) = \max(0, x) ]
    • It is highly popular due to its simplicity and effectiveness in deep learning models.

  3. Tanh 📉

    • The hyperbolic tangent function maps any real-valued number into the range (-1, 1).
    • It is similar to the sigmoid function but with a wider range and is defined as: [ \tanh(x) = \frac{e^x - e^{-x}}{e^x + e^{-x}} ]

  4. Leaky ReLU 🔥

    • Leaky ReLU is a variant of ReLU that allows a small gradient when the input is less than zero.
    • It is used to prevent dead neurons, which can occur in ReLU when the gradient becomes zero.

  5. Softmax 🏆

    • The softmax function is used in multi-class classification tasks.
    • It converts the output of a linear function into a probability distribution over the classes.

Resources

For more information on activation functions and their applications, check out our Deep Learning Basics guide.

Activation Functions