How do ReLU and Sigmoid activation functions compare?

What are the main differences between ReLU and Sigmoid activation functions?

ReLU (Rectified Linear Unit) outputs zero for negative inputs and is linear for positive inputs, while Sigmoid outputs values between 0 and 1, making it suitable for binary classification.

When should I use ReLU over Sigmoid?

ReLU is generally preferred in hidden layers of deep networks due to its ability to mitigate the vanishing gradient problem, while Sigmoid is often used in the output layer for binary classification.

Are there any drawbacks to using ReLU?

Yes, ReLU can suffer from the 'dying ReLU' problem, where neurons can become inactive and stop learning if they output zero for all inputs.

What about the advantages of using Sigmoid?

Sigmoid functions provide smooth gradients and are good for probabilistic interpretations, making them useful in binary classification tasks.