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Universal Approximation Theorem
Universal Approximation Theorem
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Foundation
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Question 1 of 5
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foundation (2/10)
conceptual
Why do neural networks need nonlinear activation functions between layers?
Hide and think first
A.
Nonlinearity is required to use gradient descent; linear activations have no gradient
B.
Nonlinearity reduces memory usage compared to purely linear networks
C.
Without nonlinearity, stacking layers collapses to a single linear map:
W
2
(
W
1
x
)
=
(
W
2
W
1
)
x
, losing the benefit of depth
D.
Nonlinearity makes the loss function convex, simplifying optimization
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