Beginner's Guide to PyTorch: A Practical Tutorial on Data Loading and Preprocessing

Data loading and preprocessing are crucial foundations for training deep learning models, and PyTorch efficiently implements this through tools like `Dataset`, `DataLoader`, and `transforms`. As a data container, `Dataset` defines how samples are retrieved—for example, built-in datasets such as MNIST in `torchvision.datasets` can be used directly, while custom datasets require implementing `__getitem__` and `__len__`. `DataLoader` handles batch loading, with core parameters including `batch_size`, `shuffle` (set to `True` during training), and `num_workers` (for multi-threaded acceleration). Data preprocessing is achieved via `transforms`, such as `ToTensor` for converting to tensors, `Normalize` for normalization, and data augmentation techniques like `RandomCrop` (used only for the training set). `Compose` allows combining multiple transformations. For practical implementation using MNIST as an example, the full workflow involves defining preprocessing steps, loading the dataset, and creating a `DataLoader`. Key considerations include normalization parameters, applying data augmentation only to the training set, and setting `num_workers=0` under Windows to avoid multi-thread errors. Mastering these skills enables efficient data handling and lays the groundwork for model training.