Azure Data Scientists Associate Practice Exam

Transfer learning is the practice of taking a model that has been pre-trained on a large dataset and adapting it for a new, often related, task. This approach is particularly advantageous when the new task has limited data available for training. Instead of building a model from scratch, which can be resource-intensive and require significant amounts of data, transfer learning allows you to leverage the learned features and representations from the pre-trained model.

In this context, fine-tuning involves adjusting the pre-trained model’s weights and biases during training on the new dataset. This process allows the model to retain relevant information learned from the original dataset while adapting specifically to the nuances of the new task. For instance, a model initially trained on a vast set of images can be fine-tuned to accurately classify a smaller dataset of specific images, saving time and computational resources while still achieving high accuracy.

By contrast, the other choices present concepts that do not accurately describe transfer learning. New algorithms for existing models or creating a model from scratch do not utilize the benefits of pre-existing learning. Combining multiple models to improve predictions refers to ensemble methods rather than transfer learning. Thus, the essence of transfer learning lies in efficiently adapting pre-trained models to new tasks, making it a powerful technique in the field of