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
Question
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
Solution
The primary purpose of regularization techniques in deep learning is to prevent overfitting. Overfitting occurs when a model learns the training data too well, to the point where it performs poorly on unseen data because it's too complex and has too many parameters. Regularization techniques add a penalty to the loss function to reduce the complexity of the model and improve its generalization ability. Therefore, the correct answer is not A (to increase model complexity), B (to introduce noise in the data), or D (to increase model variance). Instead, it's closest to C (to reduce model bias), but more accurately, it's to balance bias and variance and prevent overfitting.
Similar Questions
Q.No 9. Regularization techniques in linear regression aim to:a) Improve the interpretability of the modelb) Reduce the variance of the model and prevent overfittingc) Increase the complexity of the modeld) Decrease the bias of the model
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
why regularisation improve overfitting
What is the main goal of bias-variance tradeoff in deep learning?Question 10AnswerA.To minimize both bias and variance simultaneouslyB.To find the best-fitting model with the lowest bias and varianceC.To minimize the training errorD.To achieve perfect accuracy on the training data
ll of the following statements about Regularization are TRUE except:1 pointOptimizing predictive models is about finding the right bias/variance tradeoff.Features should rarely or never be scaled prior to implementing regularization.We need models that are sufficiently complex to capture patterns in data, but not so complex that they overfit.Regularization techniques have an analytical, a geometric, and a probabilistic interpretation.
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