vector databases embeddings applications

Vector Databases: from Embeddings to Applications

Welcome to the "Vector Databases: from Embeddings to Applications" course! This course, instructed by Sebastian Witalec, Head of Developer Relations at Weaviate, will equip you with essential skills to leverage vector databases for various applications.

Course Summary

In this course, you will delve into the world of vector databases and their applications. Here's what you can expect to learn and experience:

1. Understanding Vector Databases: Explore the role of vector databases in natural language processing, image recognition, recommender systems, and semantic search.

2. ? Embeddings and Similarity (L1): Learn how embeddings capture the meaning of data and how vector databases gauge the similarity between vectors.

3. ? Demonstration of KNN (L2)

4. ? Approximate nearest neighbours (L3)

5. Building RAG Applications (L6): Develop Retrieval Augmented Generation (RAG) applications using vector databases and LLMs.

Key Points

• ? Build practical applications, including hybrid and multilingual searches, for diverse industries.
• ? Understand vector databases and their role in developing GenAI applications without the need to train or fine-tune an LLM yourself.
• ? Learn to discern when it's best to apply a vector database to your application.

About the Instructor

? Sebastian Witalec is the Head of Developer Relations at Weaviate. With extensive knowledge in the field, Sebastian will guide you through the intricacies of vector databases.

? To enroll in the course or for further information, visit deeplearning.ai.