Latest revision as of 02:14, 6 January 2018

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Logistic regression provides the functionality of an individual neural network unit.

The regularization term is a sum from j = 1, not from j = 0, because we do not regularize the bias term θ₀.

Revision as of 02:09, 6 January 2018 (view source) Ovidiu (talk \| contribs) (→‎Overview) ← Older edit		Latest revision as of 02:14, 6 January 2018 (view source) Ovidiu (talk \| contribs) (→‎Regularized Cost Function)
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	[[Image:LogisticRegressionRegularizedCostFunction.png]]		[[Image:LogisticRegressionRegularizedCostFunction.png]]

			The regularization term is a sum from j = 1, not from j = 0, because we do not regularize the bias term θ<sub>0</sub>.