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how to decide to use dropout without checking learning curve?



2019 Community Moderator ElectionHow can I do simple machine learning without hard-coding behavior?In Weka, how to draw learning curve evaluated on both test and training set?Trend Decline Graph NormalizationCluster tendency using Hopkins statistic implementation in PythonHow to represent ROC curve when using Cross-Validationh2o, different stopping metric leads to different optimal for hyperparametersLearning rate in the Perceptron Proof and ConvergenceHow can ReLU ever fit the curve of x²?How can I calculate AUC from the ROC curve for the classification?How to use Zero-Shot Learning on text?










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Suppose I have been using dropout value of 0.5 as it gives me the optimal value . How can I explain to use dropout without checking learning curve?










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    $begingroup$


    Suppose I have been using dropout value of 0.5 as it gives me the optimal value . How can I explain to use dropout without checking learning curve?










    share|improve this question







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    Mitesh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      $begingroup$


      Suppose I have been using dropout value of 0.5 as it gives me the optimal value . How can I explain to use dropout without checking learning curve?










      share|improve this question







      New contributor




      Mitesh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







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      Suppose I have been using dropout value of 0.5 as it gives me the optimal value . How can I explain to use dropout without checking learning curve?







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