What is end-to-end learning in PyTorch?

PyTorch’s end-to-end learning refers to using the PyTorch framework to build a complete neural network model, including all steps such as data preprocessing, model building, training, and inference. By using PyTorch in end-to-end learning, it is easy to define the structure and parameters of the neural network model, and utilize PyTorch’s automatic differentiation feature for gradient descent optimization, thus achieving end-to-end training and inference processes. PyTorch’s end-to-end learning can help users quickly build and train complex deep learning models for various tasks such as image classification, object detection, speech recognition, and more.