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

With respect to image data augmentation, how does fastai presizing workand why is it advantageous?

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

Q.4 Working on computer vision problems require huge amount of image data. However, when we  have insufficient data, we use image augmentation to create more images from the existing images.Which of the following methods can be used to perform image augmentation?1. Image rotation2. Change colours of the image3. Change contrast and brightness4. All of the above

______ 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