Rectified Linear Unit

Rectified Linear Unit (ReLU) is a commonly used activation function in deep learning neural networks. It is a piecewise linear function that returns the input if it is positive, and zero otherwise. ReLU is preferred over other activation functions because it is computationally efficient and allows for faster training of deep neural networks. ReLU also helps to mitigate the vanishing gradient problem, which occurs when the gradient of the loss function becomes too small during backpropagation, making it difficult to update the weights of the network. By setting negative inputs to zero, ReLU ensures that the gradient is always non-zero, which helps to prevent the vanishing gradient problem.
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