ReLU, or Rectified Linear Unit, is an activation function commonly used in deep learning and neural networks. It is defined mathematically as ( \text{ReLU}(x) = \max(0, x) ). This means that for any input ( x ), if ( x ) is greater than zero, the output is ( x ); otherwise, the output is zero. This function is applied element-wise to the input data, making it suitable for various dimensions of input.

One of the key advantages of ReLU is its computational simplicity, which allows for faster training of neural networks. It introduces non-linearity into the model while maintaining a straightforward mathematical form, which is beneficial for optimization during training. Additionally, ReLU helps in addressing the vanishing gradient problem often encountered with other activation functions, such as sigmoid or tanh, by allowing gradients to flow through the network more effectively ³⁵.

ReLU has become a popular choice in deep learning architectures due to its effectiveness in transforming data and improving model performance.