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In contrast, the ReLU network learns a highly non-linear, well-adapted boundary that closely follows the data distribution, achieving much higher accuracy (~96%). Because ReLU preserves magnitude across layers, it enables the network to progressively bend and refine the decision surface, turning depth into actual expressive power rather than wasted capacity.

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