Editing Deep Learning Revolution (section)

=== Transfer Learning and Pre-trained Models ===
Deep learning models, especially those used for NLP tasks, can have millions or even billions of parameters. Training such models from scratch requires extensive computational resources. Transfer learning circumvents this by leveraging pre-trained models. These models, trained on vast datasets, can be fine-tuned with a smaller amount of task-specific data, accelerating development and boosting performance.<ref>Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. ''arXiv preprint arXiv:1810.04805''.</ref> <ref>Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.</ref> Transfer learning and pre-trained models have been crucial to the enhancement of ASR systems, primarily by offering significant improvements in accuracy, efficiency, and the ability to adapt models to various tasks.