This PyTorch tutorial creates and trains a simple image classifier using the MNIST dataset. It covers downloading data, creating a data loader, building a feed-forward neural network, training the model using a custom training loop, and saving the trained model. Loading and inference are left for a future video. In PyTorch, DataLoader is a crucial class for efficiently loading and batching data. iter() creates an iterator object from the DataLoader , allowing you to loop through the data. next() retrieves the next batch from the iterator. The iterator yields batches of data (inputs and targets) until the entire dataset is processed. In essence, DataLoader handles data loading, iter() prepares the data for iteration, and next() provides each batch sequentially. Let's say you have a dataset of 100 images and their corresponding labels. You create a DataLoader to handle this data in batches of 10. iter(DataLoader) creates an iterator. Calling next(iterator) once will give you a batch of 10 images and their 10 labels. Subsequent calls to next() will yield the next 10 image-label pairs until all 10 batches are processed; then a StopIteration exception is raised. This efficient batching is essential for training neural networks.