When should I use different activation functions in neural networks?

What are activation functions in neural networks?

Activation functions determine the output of a neural network node. They introduce non-linearity, allowing the network to learn complex patterns.

When should I use ReLU instead of sigmoid?

ReLU is preferred for hidden layers due to its efficiency and ability to mitigate the vanishing gradient problem, while sigmoid is often used in the output layer for binary classification.

Are there situations where I should avoid using ReLU?

Yes, ReLU can lead to dead neurons if inputs are negative. In such cases, Leaky ReLU or ELU might be better alternatives.

What about softmax? When is it used?

Softmax is used in the output layer for multi-class classification problems, as it converts logits into probabilities that sum to one.