What are activation functions in deep learning?

The question is about deep learning

Answer

Activation functions in deep learning are mathematical equations that determine the output of a neural network's node. They introduce non-linearity into the model, enabling it to learn complex patterns. Common activation functions include ReLU (Rectified Linear Unit), Sigmoid, and Tanh. ReLU is widely used due to its simplicity and effectiveness in mitigating the vanishing gradient problem. Choosing the right activation function is critical, as it affects the model's ability to converge and perform accurately.

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