Which activation function, defined as ReLU(x) = max(0, x), is commonly used in deep learning?

The Rectified Linear Unit (ReLU) activation function, defined mathematically as ReLU(x) = max(0, x), is widely used in deep learning due to its simplicity and effectiveness.

ReLU has several advantages that make it the preferred activation function for many neural network architectures. First, it allows for faster convergence during training compared to alternative functions like Sigmoid and Tanh. This is largely because ReLU does not saturate in the positive domain, meaning that it can propagate gradients effectively, which prevents issues like vanishing gradients that are common with Sigmoid and Tanh functions.

Additionally, ReLU enables sparsity in the activations since it outputs zero for any negative input, which can contribute positively to the efficiency of the network by promoting certain neurons to activate while others remain inactive. This characteristic can help the model focus on the most relevant features during learning, enhancing the overall performance of the neural network.

As a result, ReLU has become a popular choice in various deep learning applications, especially in convolutional neural networks (CNNs) and deep feedforward networks.