Fine-Tuning

Fine-tuning refers to the process of making small adjustments to a model or system to optimize its performance after initial training or configuration. In the context of machine learning and artificial intelligence, fine-tuning typically involves taking a pre-trained model (one that has already been trained on a large dataset) and further training it on a smaller, specific dataset that is relevant to a particular task. This allows the model to adapt its learned features to better fit the specifics of the new task while retaining the general knowledge it acquired during the initial training phase. Fine-tuning is widely used to enhance the accuracy and efficiency of models for tasks like image recognition, natural language processing, and other applications, enabling improved performance with less training data and time compared to training a new model from scratch.