Which of the following is a CORRECT statement for Fine-tuning?
Options:
A.
Fine-tuning is the process of adapting a pre-trained model to a new task.
B.
In fine-tuning, the parameters of the pre-trained model are altered.
C.
The key idea behind fine-tuning is to leverage the knowledge learned from the pre-trained model and fine-tune it to the new task, rather than training a model from scratch.
The correct answer is E. a, b and c only because all three statements accurately describe fine-tuning. Fine-tuning is a machine learning and AI technique where a model that has already been trained on a large dataset is further trained or adapted for a more specific task, domain, or use case. This is common in natural language processing, generative AI, computer vision, and business AI applications.
Statement A is correct because fine-tuning adapts a pre-trained model to a new task. Statement B is also correct because during fine-tuning, some or all model parameters may be updated based on task-specific data. Statement C is correct because the main advantage of fine-tuning is that it uses the general knowledge already learned by the pre-trained model instead of building a new model from the beginning. This saves time, data, compute resources, and often improves performance on specialized tasks. Therefore, the best answer is E .
