What are the differences between activation functions in neural networks?

What are the differences between activation functions in neural networks?

Activation functions determine the output of a neural network node. Common types include Sigmoid, ReLU, and Tanh.

Can you explain how the ReLU function works?

ReLU, or Rectified Linear Unit, outputs the input directly if it's positive; otherwise, it outputs zero.

What are the advantages of using ReLU over Sigmoid?

ReLU helps mitigate the vanishing gradient problem and allows models to converge faster during training.

Are there any drawbacks to using ReLU?

Yes, ReLU can suffer from the 'dying ReLU' problem where neurons can become inactive and stop learning.