What are the advantages of using ReLU in neural networks?

What are the advantages of using ReLU in neural networks?

ReLU, or Rectified Linear Unit, offers several advantages, such as faster training, reduced likelihood of vanishing gradients, and sparsity in activations.

Can you explain how it helps with the vanishing gradient problem?

Sure! ReLU allows gradients to flow through the network without diminishing, unlike sigmoid or tanh functions, which can squash gradients.

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 for all inputs.

How does ReLU compare to other activation functions?

ReLU is generally preferred for deep networks due to its simplicity and efficiency, while others like sigmoid and tanh can be better for specific tasks.