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

Why is the ReLU function so popular?

ReLU is popular because it helps mitigate the vanishing gradient problem, allowing models to train faster and perform better on large datasets.

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.

How do I choose the right activation function for my model?

Choosing the right activation function depends on your specific problem, the architecture of your network, and empirical testing. It's often useful to experiment with different functions.