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DeepLearning.AI

Jay Alammar

Luis Serrano

In this course, you will learn how to implement basic keyword search, enhance it with the rerank method, and use embeddings for dense retrieval. You will gain hands-on practice with large amounts of data and learn to overcome challenges like varying search results and accuracy. By the end of the course, you will be able to implement language model-powered search into your website or project.

Large Language Models with Semantic Search

Learn the basics of keyword search, the foundation of many search systems before the advent of language models.

Introduction to Keyword Search

Understand how to use the rerank method to rank the best responses by relevance with the query.

Enhancing Keyword Search with Rerank

Use embeddings, a powerful NLP tool, to carry out search based on the semantic meaning of the text, vastly improving results.

Implementing Dense Retrieval

Gain practical experience by working with large amounts of data and overcoming challenges like varying search results and accuracy.

Hands-on Practice with Large Data Sets

Learn to integrate language model-powered search into your website or project for enhanced user experience.

Implementing Language Model-Powered Search

This course teaches the techniques needed to leverage large language models (LLMs) into search. Enhance keyword search using Cohere Rerank, use embeddings for dense retrieval, and evaluate your effectiveness for further optimization.

Anyone who has basic familiarity with Python and wants to get a deeper understanding of key technical foundations of LLMs, and learn to use semantic search.

Python Developers

Professionals looking to enhance their keyword search capabilities using advanced NLP tools and techniques.

Data Scientists

Individuals interested in implementing language model-powered search into their websites or projects.

Web Developers

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Large Language Models with Semantic Search

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