When should I use softmax in machine learning?

Can you explain when to use softmax in machine learning?

Softmax is typically used in multi-class classification problems to convert logits into probabilities.

Why is it important to use softmax for multi-class classification?

It ensures that the output probabilities sum to 1, making them interpretable as probabilities for each class.

Are there any alternatives to softmax?

Yes, alternatives include sigmoid for binary classification and other activation functions depending on the context.

Can softmax be used in neural networks?

Absolutely! It's commonly used in the output layer of neural networks for multi-class tasks.