peremartra/Large-Language-Model-Notebooks-Course

• highreadme#1

  Reposition the README's core identity as a course

  原因：

  当前

  The current README immediately follows the H1 with a table about an associated book, delaying the clear statement that 'This practical free hands on course about Large Language models and their applications is 👷🏼in permanent development👷🏼.'

  复制粘贴的修复

  复制

  ## Build with LLMs: Hands-on Projects for Engineers, Researchers and Developers using Large Language Models, GPT, LLaMA, LangChain and Hugging Face
  
  This practical free hands-on course about Large Language models and their applications is 👷🏼in permanent development👷🏼. I will be posting the different lessons and samples as I complete them.
  
  <table>
    <tr>
      <td  width="130">
        <a href="https://amzn.to/4eanT1g">
          
        </a>
      </td>
      <td>
        <p>
          This is the unofficial repository for the book: 
          <a href="https://amzn.to/4eanT1g"> <b>Large Language Models:</b> Apply and Implement Strategies for Large Language Models</a> (Apress).
          The book is based on the content of this repository, but the notebooks are being updated, and I am incorporating new examples and chapters.
          If you are looking for the official repository for the book, with the original notebooks, you should visit the 
          <a href="https://github.com/Apress/Large-Language-Models-Projects">Apress repository</a>, where you can find all the notebooks in their original format as they appear in the book. Buy it at: <a href="https://amzn.to/3Bq2zqs">[Amazon]</a> <a href="https://link.springer.com/book/10.1007/979-8-8688-0515-8">[Springer]</a>
        </p>
      </td>
    </tr>
  </table>

  待办完成忽略
• mediumtopics#2

  Add 'course' or 'education' related topics

  原因：

  当前

  chatbots, fine-tuning-llm, hf, huggingface, langchain, large-language-models, peft-fine-tuning-llm, pruning, transformers, vector-database

  复制粘贴的修复

  复制

  chatbots, fine-tuning-llm, hf, huggingface, langchain, large-language-models, peft-fine-tuning-llm, pruning, transformers, vector-database, llm-course, hands-on-learning, educational-resource

  待办完成忽略
• lowfaq#3

  Add a FAQ section to clarify the repo's relationship with the book

  原因：

  当前

  The README contains a table and paragraph explaining the repo's relationship to the book.

  复制粘贴的修复

  复制

  ## FAQ
  
  ### Is this repository a standalone course or just code for a book?
  This repository is primarily a practical, hands-on course on Large Language Models, continuously updated with new lessons and examples. It also serves as the unofficial companion repository for the book "Large Language Models: Apply and Implement Strategies for Large Language Models" (Apress), with the notebooks here being more current than those in the official book repository.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  What are good practical resources for engineers building applications with large language models?

  你：未被推荐
  AI 推荐顺序：

  1. LangChain
  2. LlamaIndex
  3. OpenAI API Documentation and Cookbook (openai/openai-cookbook)
  4. Hugging Face Transformers Library
  5. Weights & Biases
  6. DeepLearning.AI's 'Generative AI with Large Language Models' Specialization
  7. MLflow

  AI 推荐了 7 个替代方案，却始终没点名 peremartra/Large-Language-Model-Notebooks-Course。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  How can I practically apply fine-tuning and utilize vector databases for custom large language models?

  你：未被推荐
  AI 推荐顺序：

  1. OpenAI API
  2. gpt-3.5-turbo
  3. GPT-4
  4. Pinecone
  5. text-embedding-ada-002
  6. Hugging Face Transformers (huggingface/transformers)
  7. Llama 2
  8. Mistral
  9. Falcon
  10. peft (huggingface/peft)
  11. FAISS (facebookresearch/faiss)
  12. ChromaDB (chroma-core/chroma)
  13. LangChain (langchain-ai/langchain)
  14. LlamaIndex (run-llama/llama_index)
  15. Weaviate (weaviate/weaviate)
  16. Qdrant (qdrant/qdrant)
  17. Google Cloud Vertex AI
  18. PaLM 2
  19. Gemma
  20. Google Cloud Vector Search (Matching Engine)
  21. AWS SageMaker
  22. SageMaker JumpStart
  23. Amazon OpenSearch Service
  24. Microsoft Azure Machine Learning
  25. Azure OpenAI Service
  26. Azure Cognitive Search
  27. llama.cpp (ggerganov/llama.cpp)
  28. Ollama (ollama/ollama)
  29. Milvus (milvus-io/milvus)
  30. Vald (vdaas/vald)

  AI 推荐了 30 个替代方案，却始终没点名 peremartra/Large-Language-Model-Notebooks-Course。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of peremartra/Large-Language-Model-Notebooks-Course?

  pass
  ⚠AI 未点名 peremartra/Large-Language-Model-Notebooks-Course —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts peremartra/Large-Language-Model-Notebooks-Course in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 peremartra/Large-Language-Model-Notebooks-Course

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo peremartra/Large-Language-Model-Notebooks-Course solve, and who is the primary audience?

  pass
  ⚠AI 未点名 peremartra/Large-Language-Model-Notebooks-Course —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？