When should I use softmax in the output layer of my neural network?

When should I use softmax in the output layer of my neural network?

You should use softmax in the output layer when you're dealing with multi-class classification problems. It converts raw scores into probabilities.

Can you explain how softmax works in this context?

Sure! Softmax takes a vector of raw scores and exponentiates them, then normalizes by dividing by the sum of all exponentiated scores, ensuring the output sums to 1.

What if I have a binary classification problem?

For binary classification, you can use a sigmoid activation function instead of softmax, as it outputs a single probability value.

Are there any drawbacks to using softmax?

One drawback is that softmax can be sensitive to outliers, which may lead to overconfidence in predictions if the input scores are extreme.