### [AI / ML領域相關學習筆記入口頁面](https://hackmd.io/@YungHuiHsu/BySsb5dfp)

#### [Deeplearning.ai GenAI/LLM系列課程筆記](https://learn.deeplearning.ai/)
- [Large Language Models with Semantic Search。大型語言模型與語義搜索 ](https://hackmd.io/@YungHuiHsu/rku-vjhZT)
- [Finetuning Large Language Models。微調大型語言模型](https://hackmd.io/@YungHuiHsu/HJ6AT8XG6)
- [LangChain for LLM Application Development](https://hackmd.io/1r4pzdfFRwOIRrhtF9iFKQ) 系列課程筆記
- [Building and Evaluating Advanced RAG。建立語評估進階RAG](https://hackmd.io/@YungHuiHsu/rkqGpCDca)
## [Generative AI with Large Language Models](https://www.deeplearning.ai/courses/generative-ai-with-llms/)
 

- [Week1-Generative AI use cases, project lifecycle, and model pre-training](https://hackmd.io/@YungHuiHsu/By7dsMnTp)
- [Week2-Fine-tuning and evaluating large language models](https://hackmd.io/@YungHuiHsu/HJQrU7npp)
- [Week3-Reinforcement learning and LLM-powered applications](https://hackmd.io/@YungHuiHsu/Hkbxu7h6T)

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## Week2 - 微調與評估大型語言模型<br>Fine-tuning and evaluating large language models
### Introduction - Week 2

### 2用指令微調 LLM(Fine-tuning LLMs with instruction)
#### 微調介紹 (Introduction - Week 2)

#### 指令微調 (Instruction fine-tuning)

#### 單一任務上的微調 (Fine-tuning on a single task)

#### 多任務指令微調 (Multi-task instruction fine-tuning)

#### 擴展指令模型 (Scaling instruct models)

#### 模型評估 (Model evaluation)

#### 基準 (Benchmarks)
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### 2-2. 參數高效微調(Parameter efficient fine-tuning)
#### 參數高效微調 (PEFT) (Parameter efficient fine-tuning (PEFT))

#### PEFT技術1：LoRA (PEFT techniques 1: LoRA)

#### PEFT技術2：軟提示 (Soft prompts) (PEFT techniques 2: Soft prompts)

#### Lab2 演練 (Lab 2 walkthrough)

#### Lab2 - 為對話摘要微調生成式AI模型 (Lab 2 - Fine-tune a generative AI model for dialogue summarization)

#### 第二週資源 (Week 2 Resources)