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Classroom Contents
Building a Semantic Book Recommender with Python, OpenAI, LangChain, and Gradio
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- 1
- 2 Intro
- 3 Introduction to getting and preparing text data
- 4 Starting a new PyCharm project
- 5 Patterns of missing data
- 6 Checking the number of categories
- 7 Remove short descriptions
- 8 Final cleaning steps
- 9 Introduction to LLMs and vector search
- 10 LangChain
- 11 Splitting the books using CharacterTextSplitter
- 12 Building the vector database
- 13 Getting book recommendations using vector search
- 14 Introduction to zero-shot text classification using LLMs
- 15 Finding LLMs for zero-shot classification on Hugging Face
- 16 Classifying book descriptions
- 17 Checking classifier accuracy
- 18 Introduction to using LLMs for sentiment analysis
- 19 Finding fine-tuned LLMs for sentiment analysis
- 20 Extracting emotions from book descriptions
- 21 Introduction to Gradio
- 22 Building a Gradio dashboard to recommend books
- 23 Outro
