LLM Course – Build a Semantic Book Recommender (Python, OpenAI, LangChain, Gradio)

From the creator

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: https://github.com/t-redactyl/llm-semantic-book-recommender/tree/main 🏗️ JetBrains provided a grant to make this course possible. ⭐️ Resources ⭐️ Download PyCharm: https://jb.gg/pycharm-fcc The only Python IDE you need to build data models and AI agents. Free forever, plus one month of Pro included. Kaggle datasets: https://kaggle.com/datasets 7K books dataset by Dylan Castillo: https://kaggle.com/datasets/dylanjcastillo/7k-books-with-metadata Hugging Face free NLP course: https://huggingface.co/learn/nlp-course/en/ Explanation of transformer encoder-decoder models (from Hugging Face NLP course): https://huggingface.co/learn/nlp-course/en/chapter1/7 Explanation of transformer decoder-only models (from Hugging Face NLP course): https://huggingface.co/learn/nlp-course/en/chapter1/6 Explanation of transformer encoder-only models (from Hugging Face NLP course): https://huggingface.co/learn/nlp-course/en/chapter1/5 Hugging Face Hub models page: https://huggingface.co/models OpenAI models: https://platform.openai.com/docs/models Explanation of vector index (from Weaviate): https://weaviate.io/developers

Published 2025-01-27 · Watch on YouTube ↗
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