What are activation functions and why are they important in neural networks?

What are activation functions and why are they important in neural networks?

Activation functions are mathematical equations that determine the output of a neural network node. They introduce non-linearity, allowing the network 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 advantages depending on the context.

How do activation functions affect the learning process?

Activation functions influence how well a neural network can learn from data. They help in adjusting weights during training and can prevent issues like vanishing gradients.

What happens if I don't use an activation function?

Without an activation function, a neural network would behave like a linear regression model, limiting its ability to learn complex relationships in data.

Are there any new activation functions being developed?

Yes, researchers are continuously developing new activation functions, such as Swish and Mish, which aim to improve performance in various tasks.