How do I choose the right activation function for my neural network?

What factors should I consider when choosing an activation function for my neural network?

You should consider the type of problem you're solving, the architecture of your network, and the nature of your data.

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

ReLU is linear for positive inputs and zero for negative, making it efficient, while sigmoid outputs values between 0 and 1, which can cause vanishing gradients.

When should I use softmax instead of sigmoid?

Use softmax for multi-class classification problems, as it normalizes the output to a probability distribution across multiple classes.

Are there any activation functions that are better for deep networks?

Yes, functions like Leaky ReLU and ELU can help mitigate issues like vanishing gradients in deep networks.