Explore large language models (LLMs) and vector databases, including ANN search, similarity methods, and practical skills using BERT and ChromaDB embeddings.
Beginner
15 Lessons
2h 15min
Certificate of Completion
Explore large language models (LLMs) and vector databases, including ANN search, similarity methods, and practical skills using BERT and ChromaDB embeddings.
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This course includes
This course includes
Course Overview
The course begins by introducing LLMs and their significance in modern generative AI. Learners will dive deep into vector databases, a vital tool for efficient data storage and querying in LLMs, and explore concepts like Approximate Nearest Neighbor (ANN) search, dense search, sparse search, hybrid search techniques, and similarity measures. You will then learn how to generate and store embeddings with BERT in ChromaDB, gaining hands-on experience handling complex queries and producing accurate recommendat...
What You'll Learn
Knowledge of the fundamentals of vector databases and their necessity in efficiently handling high-dimensional data within LLMs
Hands-on experience implementing Approximate Nearest Neighbor (ANN) search techniques to improve data retrieval efficiency
The ability to generate embeddings using BERT and store them in ChromaDB, a leading vector database
The ability to query vector databases to generate recommendations, bridging the gap between theory and practice in AI-driven solutions
What You'll Learn
Knowledge of the fundamentals of vector databases and their necessity in efficiently handling high-dimensional data within LLMs
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Course Content
Getting Started
Vector Databases
Guide to Generate Embeddings and Store in ChromaDB
Conclusion
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