Example Concept: Fine-tuning typically involves taking a pre-trained model, freezing some of its layers to retain learned features, and then training the remaining layers on a new dataset. This process starts with loading the pre-trained model, modifying its architecture if necessary (e.g., changing the output layer to match the new task), and then training it on the new data with a smaller learning rate to avoid large updates that could disrupt the learned features.