ReLu

ReLU, or Rectified Linear Unit, is a widely used activation function in neural networks, particularly in deep learning. 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 computational efficiency, making training faster and models more effective. ReLU has gained popularity since its introduction due to its ability to mitigate issues like vanishing gradients, thus enhancing the performance of deep learning models across various applications. Its effectiveness has led to its consistent use in modern neural network architectures.