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Fine-tuning in artificial neural networks is closely linked to transfer learning, where knowledge gained from solving one problem is applied to a new related problem. It involves taking an already trained model and tweaking it for a similar task, saving time and effort by leveraging what the model has already learned. This process is particularly useful when building models without starting from scratch.
