# Deep Learning for NLP 2025

[![pypi-image]][pypi-url]
[![version-image]][release-url]
[![release-date-image]][release-url]
[![license-image]][license-url]
[![codecov][codecov-image]][codecov-url]
[![jupyter-book-image]][docs-url]

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[repo-url]: https://github.com/entelecheia/deepnlp-2025
[pypi-url]: https://pypi.org/project/deepnlp-2025
[docs-url]: https://deepnlp2025.jeju.ai
[changelog]: https://github.com/entelecheia/deepnlp-2025/blob/main/CHANGELOG.md
[contributing guidelines]: https://github.com/entelecheia/deepnlp-2025/blob/main/CONTRIBUTING.md
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Advanced deep learning techniques for natural language processing with modern architectures and Korean language applications

- Documentation: [https://deepnlp2025.jeju.ai][docs-url]
- GitHub: [https://github.com/entelecheia/deepnlp-2025][repo-url]
- PyPI: [https://pypi.org/project/deepnlp-2025][pypi-url]

This course focuses on advanced deep learning techniques for natural language processing, covering state-of-the-art architectures including Transformers, State Space Models (Mamba, RWKV), and parameter-efficient fine-tuning methods. Students will learn prompt engineering, RAG systems, RLHF alternatives, and build practical NLP applications with Korean language datasets.

## Changelog

See the [CHANGELOG] for more information.

## Contributing

Contributions are welcome! Please see the [contributing guidelines] for more information.

## License

This project is released under the [CC-BY-4.0 License][license-url].
