Match the following to the most appropriate descriptions for each. 1: A type of machine learning where models are pre-trained usually on abundant data from one domain, and then later trained and specialized on usually a smaller dataset from another domain 2: Training a model typically on a large dataset in a given domain in order to leverage that model in other specialized domains and use cases 3: Training a pre-trained model on data in a different domain in order to create specialized models for certain tasks in the new domain 4: Layers of a deep learning architecture whose parameters are learned during the pre-training process of transfer learning匹配题

A

Pre-Training

B

Fine-Tuning

C

Transfer Learning

D

Frozen Layers

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A medical team wants an LLM that understands radiology terminology but cannot afford to retrain a model from scratch. Which approach is most appropriate?

Question at position 10 What is transfer learning in neural networks? Using a pre-trained neural network to extract features from a new datasetUsing a pre-trained neural network as a starting point for training a new modelUsing a pre-trained neural network to generate new dataNone of the above

Transfer learning is invariably effective. eg. Irrespective of the amount of data, we can always rely on transfer learning.

When attempting to transfer learn for an image captioning task, we must use a source dataset for image captioning or visual question answering, since image classification by itself is not similar enough

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