What are activation functions and how are they used in neural networks?

What are activation functions and how are they used in neural networks?

Activation functions are mathematical equations that determine the output of a neural network node. They introduce non-linearity into the model, allowing it to learn complex patterns.

Can you give me examples of common activation functions?

Sure! Some common activation functions include Sigmoid, ReLU (Rectified Linear Unit), and Tanh. Each has its own characteristics and use cases.

When should I use ReLU over Sigmoid?

ReLU is generally preferred for hidden layers because it helps mitigate the vanishing gradient problem, allowing for faster training. Sigmoid is often used in the output layer for binary classification.

Are there any drawbacks to using ReLU?

Yes, one drawback is the 'dying ReLU' problem, where neurons can become inactive and stop learning if they output zero for all inputs.