ReLu

ReLU, or Rectified Linear Unit, is a widely used activation function in neural networks that introduces non-linearity into the model. It operates by outputting the input directly if it is positive, and zero otherwise, effectively acting as a switch that allows only useful signals to pass through. This simplicity contributes to its popularity, as it helps mitigate issues like vanishing gradients, enabling faster training and improved performance. ReLU is particularly effective in deep learning applications, where it can enhance model accuracy and efficiency, making it a fundamental component in modern neural network architectures.