Activation functions decide if a neuron should activate, allowing neural networks to learn complex, non-linear patterns from data and make accurate predictions.
Why Is Non-Linearity Important?
Without activation functions, neural networks act as simple linear models, unable to capture complex data relationships. Non-linearity is essential for deep learning’s power and flexibility.
Binary Step Function: The Simplest Activation
The binary step function activates a neuron only if the input exceeds a threshold. It’s used for basic binary classification but lacks flexibility for complex tasks.
Sigmoid Function: Smooth Probability Output
The sigmoid activation function outputs values between 0 and 1, making it ideal for binary classification and probability-based predictions in neural networks.
Tanh Function: Zero-Centered Activation
The tanh (hyperbolic tangent) function outputs values between -1 and 1. It’s often used in hidden layers to provide zero-centered activations, aiding model convergence.
ReLU: The Go-To for Hidden Layers
ReLU (Rectified Linear Unit) outputs zero for negative inputs and the input itself for positive values, helping avoid vanishing gradients and speeding up training.
Choosing the Right Activation Function
Select softmax for multi-class, sigmoid for binary, or ReLU for hidden layers. The right choice boosts training speed, accuracy, and neural network performance