Examples of using Regularization in English and their translations into Korean
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Colloquial
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Ecclesiastic
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Ecclesiastic
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Programming
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Computer
Regularization(physics).
Dimensional regularization.
Regularization of it.
Zeta function regularization.
Regularization: Ridge, Lasso and Elastic Net.
Zeta function regularization.
Regularization is a technique to reduce the complexity of the model.
Pauli-Villars regularization.
One of the things I mentioned several times in the video is regularization.
Pauli-Villars regularization.
Regularization is a common method for dealing with overfitting.
CNH Driver 's License CPF Regularization.
And I think the regularization is an almost unintended side effect.
Ill-Posed Problems and Regularization.
Consequently, L1 regularization can also be used for feature selection.
Linear Model Selection and Regularization.
Regularization, CS231n Convolutional Neural Networks for Visual Recognition.
While too little noise means worse regularization.
The regularization techniques available within the Generalized Regression personality include Ridge, Lasso, adaptive Lasso, Elastic Net and the adaptive Elastic Net to help better identify X's that may have explanatory power.
And we would not discuss regularization yet.
The desire to keep the model as simple as possible(for example,strong regularization).
Now that we have added this regularization term to the objective.
When that is no longer possible, the next best solution is to use techniques like regularization.
So this is before we added this extra regularization term to the objective.
Comprehensive collection, categorization and regularization on all logs.
Sometime ago, people mostly use L2 and L1 regularization for weights.
The following simplified loss equation shows the regularization rate's influence.
And recent research has demonstrated how variations of an approach called regularization might help us achieve that goal.
A few results that stand out are that max-pooling always beat average pooling,that the ideal filter sizes are important but task-dependent, and that regularization doesn't seem to make a big different in the NLP tasks that were considered.