What are activation function derivatives in neural networks?

What are activation function derivatives in neural networks?

Activation function derivatives are the derivatives of the functions used in neural networks to introduce non-linearity. They are crucial for backpropagation.

Why are they important for training neural networks?

They are important because they help calculate the gradients needed to update the weights during training, allowing the model to learn.

Can you give examples of common activation functions?

Sure! Common activation functions include Sigmoid, ReLU (Rectified Linear Unit), and Tanh. Each has its own derivative.

How do I compute the derivative of the ReLU function?

The derivative of the ReLU function is 0 for negative inputs and 1 for positive inputs. It's defined as f'(x) = 1 if x > 0, otherwise 0.