When should I use softmax instead of sigmoid?

When should I use softmax instead of sigmoid?

You should use softmax when you have multiple classes to predict, as it normalizes the output to a probability distribution.

Can you explain how softmax works?

Sure! Softmax takes a vector of raw scores and converts them into probabilities that sum to 1, making it suitable for multi-class classification.

What about sigmoid? When is it appropriate?

Sigmoid is best for binary classification problems, as it outputs a probability between 0 and 1 for a single class.

Are there any drawbacks to using softmax?

Yes, softmax can be sensitive to outliers and may lead to overconfidence in predictions if the input scores are very different.