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DeepLearning.AI

Sebastian Witalec

In this course, you will gain the knowledge to make informed decisions about when to apply vector databases to your applications. You will explore how to use vector databases and LLMs to gain deeper insights into your data, build labs that show how to form embeddings, and use several search techniques to find similar embeddings. Additionally, you will explore algorithms for fast searches through vast datasets and build applications ranging from RAG to multilingual search.

Vector Databases: from Embeddings to Applications

Understand the basics of vector databases and their importance in various fields.

Introduction to Vector Databases

Learn how embeddings capture the meaning of data and gauge the similarity between different pairs of vectors.

Embeddings and Their Applications

Hands-on labs to form embeddings and use several search techniques to find similar embeddings.

Building Labs for Embeddings

Explore algorithms for fast searches through vast datasets.

Search Algorithms

Build applications ranging from Retrieval Augmented Generation (RAG) to multilingual search.

Developing GenAI Applications

This course provides a comprehensive understanding of vector databases and their applications in various fields such as NLP, image recognition, and recommender systems. Learn to build efficient, practical applications including hybrid and multilingual searches without needing to train or fine-tune an LLM yourself.

Anyone who’s interested in understanding and applying vector databases in their applications.

Developers

Professionals looking to enhance their knowledge in vector databases and their applications in various fields.

Data Scientists

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Vector Databases: from Embeddings to Applications

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