What is the purpose of the DataLoader class in PyTorch?

The main purpose of PyTorch’s DataLoader class is to batch load and process data. It can encapsulate a dataset object into an iterable object, allowing for batch reading and processing of data, while also automatically shuffling and batching the data.

The DataLoader class is a convenient tool for training neural network models, as training often requires splitting data into small batches, which the DataLoader class can automatically handle. It can also be used in conjunction with the Dataset class to load and process custom datasets.

In conclusion, the DataLoader class can greatly simplify the process of data handling and loading, increasing the efficiency of data processing and the readability of code.