Chapter 04.05: Transfer Learning
Transfer learning is a machine learning approach where knowledge acquired from solving one task is applied to a different but related task, typically using pretrained models. In the context of BERT, transfer learning involves leveraging the pretraining phase where the model learns general language representations on large text corpora, and then fine-tuning these representations on downstream tasks. This allows BERT to transfer the knowledge gained during pre-training to specific tasks, enabling it to achieve better performance with less labeled data compared to training from scratch.