Sigmoid vs ReLU Activation Functions: The Inference Cost of Losing Geometric Context

A deep neural network can be understood as a geometric system, where each layer reshapes the input space to form increasingly complex decision boundaries. For this to work effectively, layers must preserve meaningful spatial information — particularly how far a data point lies from these boundaries

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