What are activation functions in neural networks?
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.
Can you explain the different types of activation functions?
Sure! Common types include Sigmoid, ReLU (Rectified Linear Unit), and Tanh. Each has its own characteristics and use cases.
What is the graph of the ReLU activation function like?
The ReLU function graph is a straight line for positive inputs and flat at zero for negative inputs, resembling a ramp.
Why is the choice of activation function important?
The choice affects the model's ability to learn complex patterns. Different functions can lead to different performance outcomes.
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