Activation Function
A mathematical function applied to neuron outputs to introduce non-linearity enabling complex pattern learning.
In more detail
Without activation functions, stacked linear transformations remain linear. Activation functions enable neural networks to learn non-linear relationships. Common functions: ReLU (fast, effective), Sigmoid (smooth, historical), Tanh (centered), and others.
Examples
- ReLU (max(0, x))
- Sigmoid (logistic curve)
- Tanh (hyperbolic tangent)
- Softmax (for classification)
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