A Counterintuitive Behaviour of PyTorch DataLoader

PyTorch DataLoader applies transformations on the fly during iteration, leading to redundant computations and potentially longer training times. The suggested solution is to transform datasets beforehand using libraries like NumPy and create the DataLoader using the pre-transformed dataset. This approach circumvents the inefficiency of applying transformations during each epoch.