What is the difference between Softmax and Sigmoid?

Can you explain the difference between Softmax and Sigmoid functions?

Sure! Softmax and Sigmoid are both activation functions used in neural networks, but they serve different purposes.

What is the main use case for the Softmax function?

Softmax is typically used in multi-class classification problems to convert logits into probabilities that sum to 1.

And what about the Sigmoid function?

Sigmoid is used for binary classification tasks, mapping inputs to a range between 0 and 1, representing probabilities.

Are there any situations where I should prefer one over the other?

Yes, use Softmax for multi-class outputs and Sigmoid for binary outputs. Choosing the right function is crucial for model performance.