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