How do activation functions affect neural network performance?

What are activation functions in neural networks?

Activation functions are mathematical equations that determine the output of a neural network node, influencing how the network learns.

How do different activation functions impact performance?

Different activation functions can affect the speed of learning, convergence, and the ability to model complex patterns. For example, ReLU helps with faster training, while sigmoid can lead to vanishing gradients.

Can you give examples of common activation functions?

Sure! Common activation functions include Sigmoid, Tanh, ReLU (Rectified Linear Unit), and Softmax, each with unique properties.

Which activation function is best for deep learning?

ReLU is often preferred for deep learning due to its efficiency and ability to mitigate the vanishing gradient problem, but it depends on the specific task.