What are activation functions and how do they relate to overfitting?

What are activation functions 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.

How do activation functions affect overfitting?

Activation functions can influence overfitting by affecting how well the model generalizes to unseen data. Certain functions may lead to more complex models that fit the training data too closely.

Can you give examples of common activation functions?

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

What strategies can I use to prevent overfitting?

To prevent overfitting, you can use techniques like regularization, dropout, and early stopping, along with choosing appropriate activation functions.