info.txt

epoch = 1 forward and backward pass of ALL training samples
batch_size = number of training samples in one forward & backward pass
number of iterations = number of passes, each pass using [batch_size] number of samples
e.g. 100 samples, batch_size=20 --> 100/20 = 5 iterations for 1 epoch

Transforms can be applied to PIL images, tensors, ndarrays, or custom data
during creation of the DataSet
complete list of built-in transforms:
https://pytorch.org/docs/stable/torchvision/transforms.html
On Images
---------
CenterCrop, Grayscale, Pad, RandomAffine
RandomCrop, RandomHorizontalFlip, RandomRotation
Resize, Scale
On Tensors
----------
LinearTransformation, Normalize, RandomErasing
Conversion
----------
ToPILImage: from tensor or ndrarray
ToTensor : from numpy.ndarray or PILImage
Generic
-------
Use Lambda
Custom
------
Write own class
Compose multiple Transforms
---------------------------
composed = transforms.Compose([Rescale(256),
                               RandomCrop(224)])