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This tutorial discusses fine-tuning in artificial neural networks, which is closely related to 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 one task and adjusting it to perform a similar task. By utilizing a pre-trained neural network, we can leverage the model's existing knowledge without starting from scratch when building a new model.
