What is “transfer learning” in machine learning?
a) Reusing a pre-trained model on a new, similar task
b) Training multiple models simultaneously
c) Generating new features from existing data
d) Transferring data between different machines
Answer:
a) Reusing a pre-trained model on a new, similar task
Explanation:
Transfer learning is a technique in machine learning where a pre-trained model is reused for a new, similar task. Instead of training a model from scratch, the knowledge learned from the original task is applied to a new task, saving time and computational resources.
Transfer learning is especially useful when you have limited data for the new task but can leverage a model trained on a large dataset for a related problem. For example, a pre-trained image classifier can be fine-tuned to classify different types of images with minimal retraining.
This technique is widely used in deep learning, especially for tasks like image recognition, natural language processing, and other domains where large datasets are required to train accurate models.