What is the difference between sigmoid and softmax functions?

What is the difference between sigmoid and softmax functions?

The sigmoid function outputs a value between 0 and 1, making it suitable for binary classification. Softmax, on the other hand, outputs a probability distribution across multiple classes, summing to 1.

When should I use sigmoid instead of softmax?

Use sigmoid when you have a binary classification problem. For multi-class classification, softmax is the better choice.

Can you give an example of when to use softmax?

Sure! Softmax is commonly used in the final layer of a neural network for tasks like image classification, where you want to predict one class out of many.

Are there any drawbacks to using these functions?

Yes, sigmoid can cause vanishing gradients in deep networks, while softmax can be sensitive to outliers in the input data.