peremartra/Large-Language-Model-Notebooks-Course

• highreadme#1

  Reposition the README's core identity as a course

  Why:

  CURRENT

  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👷🏼.'

  COPY-PASTE FIX

  Copy

  ## 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>

  OpenDoneDismiss
• mediumtopics#2

  Add 'course' or 'education' related topics

  Why:

  CURRENT

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

  COPY-PASTE FIX

  Copy

  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

  OpenDoneDismiss
• lowfaq#3

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

  Why:

  CURRENT

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

  COPY-PASTE FIX

  Copy

  ## 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.

  OpenDoneDismiss

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

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

  you: not recommended
  AI recommended (in order):

  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 recommended 7 alternatives but never named peremartra/Large-Language-Model-Notebooks-Course. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

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

  you: not recommended
  AI recommended (in order):

  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 recommended 30 alternatives but never named peremartra/Large-Language-Model-Notebooks-Course. This is the gap to close.

  Show full AI answer

• 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 did not name peremartra/Large-Language-Model-Notebooks-Course — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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

  pass
  ✓AI named peremartra/Large-Language-Model-Notebooks-Course explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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

  pass
  ⚠AI did not name peremartra/Large-Language-Model-Notebooks-Course — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?