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This video explains what fine-tuning is and how it relates to transfer learning. Transfer learning involves using knowledge gained from solving one problem to help in solving a new, related problem. Fine-tuning is a process of taking a pre-trained model and adjusting it to perform a similar task, saving time and effort compared to building a new model from scratch. Fine-tuning is beneficial when the original task is similar to the new task, allowing us to utilize the knowledge already stored in the neural network.
