LLM Course - Build a Semantic Book Recommender

Ever wondered how AI-powered book recommendation systems work? Want to go beyond simple keyword searches and build a **semantic book recommender** that actually understands books?
This book takes you on an exciting and practical journey into the world of **LLMs (Large Language Models), embeddings, and vector search**-but with a twist. You won't just be reading dry academic explanations. Instead, you'll follow **Anya Sharma**, a recent CS graduate who, thanks to a mix of ambition and spite, decides to build an AI recommender to out-code her overconfident classmate, Marcus.
Through **hands-on coding, humor, and real-world AI applications**, you will:Master **embeddings and vector search** to find similar books based on content, not just keywords.Use **OpenAI's API** to generate intelligent recommendations.Implement **FAISS** for real-time, scalable search.Develop a functional **Gradio UI** that makes AI look pretty (or at least usable).Handle AI hallucinations, optimize queries, and avoid dumb mistakes that cost money.If you've ever struggled with AI projects, debugging nightmares, or just want a **fun and practical guide to semantic search**, this book is for you.
What You'll Learn (Without Losing Your Mind):**How embeddings work:** Stop guessing and start understanding how AI represents text in multi-dimensional space.**The power of cosine similarity:** Discover why AI can compare books based on meaning.**LLMs in action:** Summon GPT-4 like a dark sorcerer and make it analyze books intelligently.**Optimizing AI for cost and performance:** Because nobody wants a recommender that bankrupts them.This isn't just another AI book. It's a **practical, entertaining, and slightly chaotic** ride through building a real-world **semantic book recommender**-one that doesn't suck.

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