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AI & Machine Learning · Foundational

Activation Function

A nonlinear function applied to a neuron's output that lets networks learn complex patterns.

Definition

Without nonlinear activations, stacked layers collapse into a single linear transformation. Common choices are ReLU and GELU in hidden layers, sigmoid for binary outputs and softmax for multiclass outputs.

Exam tip

Output layer activation must match the task: sigmoid (binary/multilabel), softmax (multiclass), linear (regression).

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