Hello ,
New to PyTorch and deep learning I have Four questions :
1- Is it better to scale all your data then split to training and testing sets or do it otherwise.
2- How to use effectively use data augmentation
3- I have missing values in my datasets NaN I just removed the lines where they appeared (8 out of 9230) so im my opinion they don’t have too much impact or do they.
4- I’m doing classification using different types of neural nets , I have 12 classes in total but some are too rare compared to other , is there a way to make the model more robust to rare classes.
And Thank you.