# Case 01

주차별로 나누기

- [01. LLM 지도](https://slashpage.com/llm-ai-application-study-2024/n8pw9x2zp5g672g7yrqv)
- [02. LLM 중추, 트래스포머 아키텍처 살펴보기](https://slashpage.com/llm-ai-application-study-2024/93nzyxmd4gzy7mwk6r45)
- [03. 트랜스포머 모델을 다루기 위한 허킹페이스 트랜스포머 라이브러리](https://slashpage.com/llm-ai-application-study-2024/xjqy1g2vqg84jm6vd54z)
- [04. 말 잘 듣는 모델 만들기](https://slashpage.com/llm-ai-application-study-2024/ywk9j7295xkqr2gpqvnd)
- [05. GPU 효율적인 학습](https://slashpage.com/llm-ai-application-study-2024/4w67rj24nepr125yq8ep)
- [06. sLLM 학습하기](https://slashpage.com/llm-ai-application-study-2024/qpv5x42783jn3mkyn3dw)
- [07. 모델 가볍게 만들기](https://slashpage.com/llm-ai-application-study-2024/d367nxm34p5jv2j98pv1)
- [08. sLLM 서빙하기](https://slashpage.com/llm-ai-application-study-2024/qrx6zk25j6ek9mv314y5)
- [09. LLM 애플리케이션 개발하기](https://slashpage.com/llm-ai-application-study-2024/1q3vdn2pqnr4gmxy49pr)
- [10. 임베딩 모델로 데이터 의미 압축하기](https://slashpage.com/llm-ai-application-study-2024/dk58wg2ejz91vmnqevxz)
- [11. 자신의 데이터에 맞춘 임베딩 모델 만들기: RAG 개선하기](https://slashpage.com/llm-ai-application-study-2024/91kwev26nj3dr2y46jpg)
- [12. 벡터데이터베이스로 확장하기: RAG 구현하기](https://slashpage.com/llm-ai-application-study-2024/dwy5rvmj134v12p46zn9)
- [13. LLM 운영하기](https://slashpage.com/llm-ai-application-study-2024/7916x82rqwxepm4kpyg3)
- [14. 멀티 모달 LLM이란](https://slashpage.com/llm-ai-application-study-2024/4z7pvx2k965g12ek8653)
- [15. LLM 에이전트](https://slashpage.com/llm-ai-application-study-2024/y9e1xp2xxd8n527k35vz)
- [16. 새로운 아키텍처](https://slashpage.com/llm-ai-application-study-2024/7vgjr4m15v9d1mdwpy86)
- [17. 부록](https://slashpage.com/llm-ai-application-study-2024/36nj8v2wdz838m5ykq9z)

For the site tree, see the [root Markdown](https://slashpage.com/llm-ai-application-study-2024.md).