Neural network training involves managing large datasets with PyTorch’s Dataset and DataLoader for efficient batching and shuffling. Proper device management moves models and data to GPU memory. The training loop includes forward passes, loss computation, backpropagation, and optimizer steps, with validation using model.eval() and no_grad().