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Discover how to build an intelligent book recommendation system using the power of large language models and Python. Learn to transform book descriptions into mathematical representations that enable precise content-based matching. By the end of this course, you'll have built a recommendation engine that helps readers discover their next favorite book.
💻 Code from this tutorial:
🏗️ JetBrains provided a grant to make this course possible.
⭐️ Resources ⭐️
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Code: PyCharm4FreeCodeCamp
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Kaggle datasets:
7K books dataset by Dylan Castillo:
Hugging Face free NLP course:
Explanation of transformer encoder-decoder models (from Hugging Face NLP course):
Explanation of transformer decoder-only models (from Hugging Face NLP course):
Explanation of transformer encoder-only models (from Hugging Face NLP course):
Hugging Face Hub models page:
OpenAI models:
Explanation of vector index (from Weaviate):
LangChain Python docs:
LangChain chat model integrations:
OpenAI billing page:
OpenAI API keys page:
Explanation of zero-shot classification (from Hugging Face):
Information about fine-tuned emotion classification model:
Getting started with Gradio:
Gradio playground:
Gradio themes:
Further work by Jodie about LLMs
Talk from GOTO Amsterdam giving an overview of LLMs:
Talk from NDC Oslo about whether LLMs are showing signs o
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2025/01/27
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