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This video discusses fine-tuning in artificial neural networks, which is closely linked with transfer learning. Transfer learning involves using knowledge gained from solving one problem and applying it to a new related problem. Fine-tuning is a process of taking a pre-trained model for a specific task and tweaking it to perform a similar task. This allows us to leverage what the model has already learned without starting from scratch when building a new model.
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