Activation functions are an essential component of artificial neural networks. They introduce non-linearities into the network, allowing it to learn complex patterns from data.

Common Activation Functions

  • Sigmoid: Maps any real-valued number into the (0, 1) interval. It is useful for binary classification problems.
    • Sigmoid Function
  • ReLU (Rectified Linear Unit): Outputs the input directly if it is positive, otherwise, it outputs zero. It is computationally efficient and helps in avoiding vanishing gradients.
    • ReLU Function
  • Tanh (Hyperbolic Tangent): Maps any real-valued number into the (-1, 1) interval. It is similar to the sigmoid function but can help in the convergence of the network.
    • Tanh Function

Activation Functions in Neural Networks

Activation functions play a crucial role in neural networks by introducing non-linearities. They help the network to learn complex patterns and make predictions.

For more information on neural networks, you can visit our Neural Networks Tutorial.