A. ReLU (Rectified Linear Activation) is a widely used activation function in neural networks. It introduces non-linearity, aiding in complex pattern recognition. By avoiding vanishing gradient issues, ReLU accelerates training convergence. However, its "dying ReLU" problem led to variations like Leaky ReLU, enhancing its effectiveness in deep learning models.