How do I tune activation functions in neural networks?

How do I tune activation functions in neural networks?

Tuning activation functions involves selecting the right function and adjusting parameters to improve model performance. Common functions include ReLU, sigmoid, and tanh.

What factors should I consider when choosing an activation function?

Consider the type of problem (classification vs regression), the architecture of your network, and the nature of your data.

Can you explain the differences between ReLU and sigmoid?

ReLU is faster and helps mitigate the vanishing gradient problem, while sigmoid outputs values between 0 and 1, making it suitable for binary classification.

How can I implement these changes in my model?

You can implement changes by modifying the activation function in your model's architecture code, typically in libraries like TensorFlow or PyTorch.