What are activation functions and how do they affect optimization in neural networks?

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.

Why are activation functions important for optimization?

They are crucial because they help the model learn and approximate complex functions. Without them, the network would behave like a linear model, limiting its capability.

Can you give examples of common activation functions?

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

How do these functions impact the training process?

They affect the convergence speed and the ability to escape local minima during training. For instance, ReLU helps mitigate the vanishing gradient problem.

What should I consider when choosing an activation function?

Consider the type of problem you're solving, the architecture of your network, and the specific characteristics of the activation functions, like their derivatives.