______ is like ensembling and it reduces the risk of the model fitting noise or outliers in the training data too closely, thus improving its performance on unseen data.*a) Dropoutb) Batch normalizationc) Changing the model architectured) Cross validation
Question
______ is like ensembling and it reduces the risk of the model fitting noise or outliers in the training data too closely, thus improving its performance on unseen data.*a) Dropoutb) Batch normalizationc) Changing the model architectured) Cross validation
Solution
The correct answer is a) Dropout. Dropout is a regularization technique for reducing overfitting in neural networks. This technique temporarily drops out, or ignores, some neurons during training, which helps to make the model more robust and less likely to overfit to the training data. This is similar to ensembling, where multiple models are trained and their predictions are averaged to improve performance. By ignoring some neurons, dropout effectively creates a different model architecture each time, which can help to capture a wider range of patterns in the data and improve performance on unseen data.
Similar Questions
Which of the following is used to overcome from the underfitting?Use data augmentation techniqueRemove outliers in the training setAdd more features to the dataSelect a model with lesser features
What is used to refine the models during training?Batch NormalizationAdam OptimizerAll of the given optionsConv2DLeakyReLU
What is the purpose of data augmentation in deep learning? Question 14Answera. Reducing the learning rate during trainingb.Expanding the training dataset by applying various transformations to the existing datac. Increasing the complexity of the modeld. Adding noise to the data for regularization
What is the primary purpose of regularization techniques in deep learning?Question 2AnswerA.To increase model complexityB.To introduce noise in the dataC.To reduce model biasD.To increase model variance
What does the dropout regularization technique do during training?Question 3AnswerA.Increases the learning rate dynamically during trainingB.Adds a penalty term to the loss function based on weight magnitudesC.Adds random noise to the input dataD.Sets a fraction of randomly chosen activations to zero
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