What are the applications of activation functions in neural networks?

What are the applications of activation functions in neural networks?

Activation functions are crucial in neural networks as they introduce non-linearity, allowing the model to learn complex patterns.

Can you give me examples of different activation functions?

Sure! Common activation functions include ReLU, Sigmoid, and Tanh, each with unique properties and use cases.

How does the choice of activation function affect model performance?

The choice of activation function can significantly impact convergence speed, accuracy, and the ability to model complex relationships.

Are there any specific scenarios where one activation function is preferred over others?

Yes, for instance, ReLU is often preferred in hidden layers due to its efficiency, while Sigmoid is used in binary classification outputs.