Fine-tuning is a technique in machine learning that involves taking a pre-trained model and adapting it to a new task or dataset. The goal of fine-tuning is to leverage the knowledge already learned by the model during its original training and then tailor that general knowledge to a more specific application.

In the fine-tuning process, certain parameters of the pre-trained model are modified through continued training on the new data. While in many cases, only the weights of the final layers are adjusted, it is possible to fine-tune multiple layers or even the entire model, depending on the application and available data. This allows the model to preserve its generic feature extraction abilities while adapting the output to the new task.

Fine-tuning typically requires less data than training a model from scratch, though it’s essential that this data is representative of the specific task at hand. Using fine-tuning, practitioners often achieve better performance on the specialized task, as they build on prior learned knowledge. Common applications include adapting image classification or speech recognition models to recognize new classes, or adjusting language models to generate text in a new style or topic.

Overall, fine-tuning allows practitioners to benefit from transfer learning and sidestep the need to train large models from scratch. By building on extensive pre-training and then customizing select parts of the model, fine-tuning strikes a balance between generalization and specialization.