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This video discusses the concept of fine-tuning in artificial neural networks, which is closely related 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. By utilizing a pre-trained neural network, we can benefit from the model's prior learning without starting from scratch.
