What are the differences between ReLU and Sigmoid activation functions?

What are the differences between ReLU and Sigmoid activation functions?

ReLU (Rectified Linear Unit) and Sigmoid are both activation functions used in neural networks. ReLU outputs the input directly if it's positive; otherwise, it outputs zero. Sigmoid, on the other hand, squashes the input to a range between 0 and 1, making it useful for binary classification.

When should I use ReLU over Sigmoid?

You should use ReLU when you want to avoid the vanishing gradient problem, especially in deep networks. It's generally preferred for hidden layers. Sigmoid is better for output layers in binary classification tasks.

Are there any drawbacks to using ReLU?

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

Can you give me an example of when to use Sigmoid?

Sure! Sigmoid is often used in the output layer of a binary classification model, where you want to predict probabilities.