Transfer learning is a machine learning technique that allows a model to leverage knowledge learned from one task to improve its performance on another related task. In transfer learning, a pre-trained model that has already been trained on a large dataset is used as a starting point, and then it is fine-tuned or adapted to a different but related task with a smaller dataset.
In simpler terms, imagine you are learning to play a musical instrument. Let’s say you have already become proficient at playing the piano. Now, if you decide to learn to play another instrument like the guitar, you can transfer some of your knowledge and skills from playing the piano. For example, your understanding of musical notes, rhythm, and coordination can be applied to the guitar as well. This means you don’t have to start from scratch; you can build upon what you already know and learn the new instrument faster.
Similarly, in transfer learning for AI models, a pre-trained model has already learned useful features and patterns from a large dataset. This knowledge can be transferred to a new task, even if the new task has a smaller dataset. By leveraging the pre-existing knowledge, the model can learn more efficiently and achieve better performance on the new task compared to training it from scratch.
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