What are activation functions and how do I choose the right one?

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 is it important to choose the right activation function?

Choosing the right activation function is crucial because it affects the network's ability to learn and model data. Different functions can lead to different performance outcomes.

What are some common activation functions I should consider?

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

How do I decide which activation function to use for my model?

Consider factors like the type of problem you're solving, the architecture of your network, and empirical performance. Experimenting with different functions can also help you find the best fit.