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What are activation functions and why are they used in neural networks?
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Answer / Gautam Singh
Activation functions are mathematical equations applied to the output of a neuron (or node) in a neural network. They introduce non-linearity, allowing the network to learn complex relationships between input and output data. Common activation functions include sigmoid, ReLU, and tanh. Activation functions are crucial for learning complex patterns and avoiding overfitting by introducing non-linearities.
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