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In this video, we will discuss fine-tuning and how it can benefit building and training artificial neural networks. Fine-tuning is closely related to transfer learning, where knowledge from solving one problem is applied to a related new problem. For example, knowledge of recognizing cars can be used for recognizing trucks. Fine-tuning involves taking a pre-trained model and adjusting it to perform a similar task. This allows us to benefit from the pre-trained model's learning without starting from scratch when building a new model from an artificial neural network.