Advanced deep learning techniques for natural language processing with modern architectures and Korean language applications
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Deep Learning for NLP 2025
Advanced deep learning techniques for natural language processing with modern architectures and Korean language applications
- Documentation: https://deepnlp2025.jeju.ai
- GitHub: https://github.com/entelecheia/deepnlp-2025
- PyPI: https://pypi.org/project/deepnlp-2025
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.
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Contributions are welcome! Please see the contributing guidelines for more information.
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This project is released under the CC-BY-4.0 License.
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