A practical example of fine-tuning involves adapting an image classification model. First, a source model is pre-trained on a large source dataset, such as ImageNet. To adapt this model for a specific task, like classifying images in a smaller target dataset of chairs, a target model is constructed by copying the source model's parameters and replacing its output layer. The new output layer is then trained from scratch on the chair dataset, while the remaining layers are fine-tuned, allowing the target model to leverage the broad visual features learned from the source dataset.