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Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. Recent years have seen a plethora of pre-trained models such as ULMFiT, BERT, GPT, etc being open-sourced to the NLP community. Given the size of such humungous models, it's nearly impossible to train such networks from scratch considering the amount of data and computation that is required. This is where a new learning paradigm "Transfer Learning" kicks in.