What is fine-tuning in AI?

Fine-tuning in AI refers to the process of taking a pre-trained model, such as a deep neural network trained on a large dataset, and adapting it to a specific task with a smaller dataset. This is done by continuing the training of the model on the new task, adjusting the model's weights to improve its performance on the task.

Why is fine-tuning used?

Fine-tuning is used to leverage the knowledge learned by the pre-trained model, which can improve the performance of the model on the new task, particularly when the new task has limited data. It can also save computational resources, as it is typically faster and cheaper than training a model from scratch.

Fine-tuning is a common practice in many areas of AI, such as computer vision and natural language processing.

What are the challenges of fine-tuning?

While fine-tuning can be effective, it also has challenges. It requires careful selection of the learning rate and other hyperparameters, as the model can easily overfit to the new task. It also assumes that the pre-trained model's knowledge is relevant to the new task, which may not always be the case.