Choose among the following options 1~6: 1) Regression; 2) Classification; 3) Semi-supervised Learning; 4) Unsupervised Learning; 5) Transfer Learning; 6) Reinforcement Learning, Q: If we have some labelled data, and we also have data not related to the task considered (can be either labeled or unlabeled). Which machine learning technique will be most suitable? Answer: [Fill in the blank], Multiple fill-in-the-blank

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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

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

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

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