What is the purpose of DataLoader in PyTorch?

The DataLoader in PyTorch is a practical tool for loading data from a dataset and generating small batches of data. It divides the dataset into small batches, allowing for more efficient use of data when training neural networks. The DataLoader can also perform operations such as data randomization, batch processing, and parallel loading to help users handle large datasets more easily during training. By utilizing the DataLoader, users can conveniently combine datasets with neural network models to achieve fast loading and training of data.