What are the best practices for choosing activation functions in neural networks?

What are the best practices for choosing activation functions in neural networks?

When choosing activation functions, consider factors like the type of problem, the architecture of your network, and the desired output.

Can you explain the differences between ReLU and sigmoid functions?

ReLU is linear for positive inputs and zero for negative, making it efficient, while sigmoid outputs values between 0 and 1, which can lead to vanishing gradients.

Are there specific scenarios where one is preferred over the other?

Yes, ReLU is often preferred in hidden layers due to its efficiency, while sigmoid is commonly used in the output layer for binary classification.

What about using softmax for multi-class classification?

Softmax is ideal for multi-class classification as it converts logits into probabilities, ensuring the outputs sum to one.

How do I decide which activation function to use for my specific model?

Evaluate your model's architecture, the nature of your data, and experiment with different functions to see which yields the best performance.