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The article discusses the impact of transfer learning on the field of Natural Language Processing (NLP). Transfer learning involves using pre-trained models on large datasets and fine-tuning them for specific tasks, resulting in improved performance and faster development. This approach has revolutionized NLP and is now expanding to other areas such as computer vision. The article also explains the two-step process of transfer learning: pre-training and fine-tuning.
