Pre-training in deep learning involves optimizing a neural network on an initial task before it is fine-tuned for a specific application. The core idea is that a model trained for one purpose can be successfully adapted for another. This strategy avoids the need to build complex models from the ground up, a significant advantage when dealing with tasks that have scarce labeled data. By utilizing tasks with more abundant supervision signals, pre-training reduces dependence on task-specific labels and facilitates the creation of more versatile, general-purpose models.