persistent question when working with deep learning systems is: "How much data is required to ensure that a deep learning system does not overfit?"If the amount of available training data drastically decreases, what is the best way to accommodate this change?$1$ ptsIt is better to use a high complexity machine, but data augmentation techniques should be applied to increase the data size before the training process.Data augmentation should be used, regardless of the machine complexity, because it is an error$-$ free way to avoid averfitting.It is better to use a high complexity machine without applying data augmentation techniques.It is better to use a simpler machine