Jeremy was talking about Bernoulli random variables specifically in the context of drop-out, in which you are either using an activation (multiply by 1) or dropping it completely (multiply by 0). We aren’t using a Bernoulli for our category predictions.
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For example if you are trying to predict an intercept (i.e. and offset) then absolute error might be more important. If you are trying to predict a slope (i.e a scale factor) then fractional error would be more important.
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Do we train on the original image as well as the augmented one?
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Usually the p_affine and p_lighting are not 1, so sometimes your images are the original ones.
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Is there any way in the docs (or elsewhere) to see what to do instead when certain commands / modules have been deprecated?
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I feel obligated to say I don’t like cats. I’m just shamelessly trying to get people to read our docs by putting kittens in them.
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What about using these kinds of data augmentation techniques on abstract images, like spectrograms?
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What do you mean by afterward?
So if I train only one epoch, the network trains on only one version of the image (which could be an original or an augmented one)?
use Pandas, everybody loves Pandas 
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Is there an roc_auc metric that we can pass to a Learner in case of an imbalanced classes in binary classification tasks?
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After the first layer, there was a sequence of linear/ReLU/batchnorm.
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They should be used conscientiously by the user, trying to replicate the real world (test set). Some types of augmentation are not useful on a specific dataset (e.g. a cat upside down).
Exactly.
what is padding, what is it used for?
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Padding is adding a border around your image (which can be a reflection of the image). With or without padding will change the size of the next layer in a network.
Here, Jeremy has used padding to make the data augmentation clearer (e.g. how it was rotated or distorted).
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why would you do that?
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