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VisualizationsNeural Networks & How They Learn

Vanishing Gradients

Slide a point along the sigmoid curve and read its slope. It is never above 0.25 and nearly zero at the ends, so gradients shrink layer by layer.

input 0.0slope 0.250

The steepest sigmoid ever gets is 0.25 — and out at the flat ends it is very nearly zero.

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