What are activation functions?
An example of an AI chatbot conversation about activation functions 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.
Why are activation functions important?
They are crucial because they allow the network to learn complex patterns and make decisions based on the input data.
Can you give me examples of common activation functions?
Sure! Common activation functions include Sigmoid, ReLU (Rectified Linear Unit), and Tanh (Hyperbolic Tangent).
How do I choose the right activation function for my model?
Choosing the right activation function depends on the specific problem and architecture. ReLU is often a good default for hidden layers.
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