PyTorch DataLoaders Overview and Examples (batch_size, shuffle, num_workers, pin_memory, drop_last)

PyTorch DataLoaders are super powerful and a critical part of any PyTorch deep learning project. They help you automate the creation of mini-batches of data for the training process and also speed up up the overall training and testing process through parallelization.
In this video, you will get a quick overview of how to use PyTorch Dataloaders. Dataloaders are an important component when building deep learning applications using the PyTorch library. Dataloaders will create mini-batches using your dataset for your training loop. They will help you speed up the training process and also provide automatic data shuffling. You will be given several examples of how to use Dataloaders when training and testing your models and explore all the common PyTorch Dataloader settings including dataset, batch_size, shuffle, num_workers, pin_memory and drop_last.
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