What are the best practices for choosing activation functions in neural networks?

What are the best practices for choosing activation functions in neural networks?

When choosing activation functions, consider factors like the type of problem, the architecture of the network, and the desired output.

Can you explain the differences between ReLU and Sigmoid activation functions?

ReLU is preferred for hidden layers due to its efficiency and ability to mitigate vanishing gradients, while Sigmoid is often used in output layers for binary classification.

Are there any situations where I should avoid using ReLU?

Yes, ReLU can suffer from the 'dying ReLU' problem, where neurons can become inactive. In such cases, consider using Leaky ReLU or ELU.

What about using Softmax for multi-class classification?

Softmax is ideal for multi-class classification as it outputs probabilities for each class, ensuring they sum to one.