Robotics and Bioinspired Systems
An activation function is a mathematical operation applied to a neuron's output in a neural network that determines whether it should be activated or not based on the input it receives. This function introduces non-linearity into the model, allowing it to learn complex patterns and relationships in data. Activation functions play a crucial role in how well a neural network performs, affecting everything from convergence speed to the final accuracy of predictions.
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