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What is transfer learning in AI?

Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task. It is an important technique in the field of deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks given the vast compute and time resources required to develop neural network models on these problems and from the huge jumps in skill that they provide on related problems.

How does transfer learning work?

Transfer learning works by leveraging the patterns learned by a model on a source task to improve the learning of a model on a target task. This is typically done by using the learned model as a feature extractor and training a new model on top of the extracted features.

Transfer learning is particularly effective when the source and target tasks are similar, as the patterns learned by the source model are likely to be relevant to the target task.

What are the advantages of transfer learning?

Transfer learning can significantly improve the performance of a model on a target task, particularly when the target task has limited data. It can also reduce the computational resources required to train the model, as the model can leverage the learning from the source task rather than starting from scratch.

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