The dying ReLU problem occurs when a large gradient flows through a ReLU%20Function.md) neurone, causing the weights to update in such a way that the neurone will almost always output zero. This can happen if the weights are updated in such a way that the neurone's output is always negative, and the gradient of the ReLU function is zero for negative inputs. This can cause a decrease in the model's performance, as the neurone will no longer learn from the data or contribute to the model's output.

Mitigation

To mitigate the dying ReLU problem, leaky ReLU or parametric ReLU activation functions are used. Additionally it's important to pick a suitable sized learning rate to prevent large gradients from causing the dying ReLU problem.