What are the differences between sigmoid and tanh activation functions?

What are the differences between sigmoid and tanh activation functions?

The sigmoid function outputs values between 0 and 1, while the tanh function outputs values between -1 and 1. This affects how they handle input data.

Which one is better for deep learning models?

Tanh is often preferred for hidden layers in deep networks because it centers the data around zero, which can lead to faster convergence.

Are there any drawbacks to using sigmoid?

Yes, sigmoid can cause vanishing gradients, especially in deep networks, making it harder for the model to learn.

When should I use tanh instead of sigmoid?

Use tanh when you need outputs that are zero-centered, especially in hidden layers. Sigmoid can be used in the output layer for binary classification.