What are the common mistakes with activation functions in neural networks?

What are the common mistakes with activation functions in neural networks?

Common mistakes include using inappropriate activation functions, not considering the output layer's requirements, and failing to normalize inputs.

Can you explain why using the wrong activation function is a mistake?

Using the wrong activation function can lead to poor model performance, as it may not capture the underlying data patterns effectively.

What about the choice of activation function for the output layer?

The output layer's activation function should match the task: softmax for multi-class classification, sigmoid for binary classification.

How can I avoid these mistakes?

To avoid mistakes, always analyze your data, understand the problem type, and experiment with different activation functions.