Vanishing Gradient Problem

Easy

neural-networks Google DeepMind Meta

Multiple Choice

The sigmoid function clips large values, discarding gradient information The chain rule multiplies many small sigmoid derivatives, causing gradients to shrink exponentially with depth The bias terms in each layer absorb most of the gradient signal Sigmoid outputs are always positive, which causes gradients to oscillate and cancel out

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