What are the advantages of ReLU?

What are the advantages of using the ReLU activation function in neural networks?

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

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

Sure! ReLU allows gradients to flow through the network without saturating, which helps maintain strong gradients during backpropagation.

Are there any downsides to using ReLU?

Yes, one downside 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 like sigmoid or tanh?

ReLU generally performs better than sigmoid and tanh in deep networks due to its linearity and reduced risk of vanishing gradients.