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