How does ReLU work?

Can you explain how the ReLU activation function works?

Sure! ReLU stands for Rectified Linear Unit. It outputs the input directly if it is positive; otherwise, it outputs zero.

Why is ReLU preferred over other activation functions?

ReLU is preferred because it helps mitigate the vanishing gradient problem and allows models to converge faster.

Are there any drawbacks to using ReLU?

Yes, one drawback is the 'dying ReLU' problem, where neurons can become inactive and stop learning if they output zero.

How can we address the dying ReLU problem?

One way to address it is by using variants like Leaky ReLU or Parametric ReLU, which allow a small, non-zero gradient when the input is negative.