How does an activation function work in neural networks?

Can you explain what an activation function is in neural networks?

Sure! An activation function is a mathematical operation applied to the output of a neuron in a neural network. It determines whether the neuron should be activated or not, essentially introducing non-linearity into the model.

Why is non-linearity important in neural networks?

Non-linearity allows neural networks to learn complex patterns and relationships in data. Without it, the network would behave like a linear model, limiting its ability to solve complex problems.

What are some common types of activation functions?

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

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

Choosing the right activation function depends on the specific problem you're solving. For example, ReLU is often preferred for hidden layers due to its efficiency, while Sigmoid is commonly used in binary classification tasks.