What are activation functions in deep learning?

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Answer

Activation functions in deep learning are mathematical functions that determine the output of a neural network node. They introduce non-linearity into the model, enabling it to learn complex patterns. Common activation functions include ReLU (Rectified Linear Unit), which is efficient and helps mitigate the vanishing gradient problem, and sigmoid and tanh, which are used in specific scenarios. Choosing the right activation function is crucial for model performance, as it affects convergence speed and the model's ability to capture intricate data relationships.

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