What are the primary benefits and drawbacks of using ReLU versus Sigmoid activation functions in a deep neural network?

ReLU (Rectified Linear Unit) and Sigmoid are common activation functions used in deep neural networks, each with its own benefits and drawbacks. A primary benefit of ReLU is its ability to alleviate the vanishing gradient problem, which can hinder the training of deep networks. The derivative of ReLU is either 0 or 1, meaning gradients are less likely to be squashed as they propagate through the layers. This allows for faster and more effective training, especially in deep networks. Sigmoid, on the other hand, has a derivative that ranges from 0 to 0.25, which can le....