Project

Working with Scientific Papers

Challenging

8 completions

~ 14 hours

4.4

After completing this project, you will gain PostgreSQL skills, focusing on SELECT, WHERE, and GROUP BY. You will learn how to extract information and perform similarity searches using vector embeddings with pg-vector. This project will equip you with skills to analyze academic research papers, including working with machine learning embeddings.

Provided by

JetBrains Academy

About

In this project, you'll explore the rich collection of research articles on arXiv while gaining hands-on experience with SQL. You'll start by mastering basic SQL queries to extract and analyze article metadata, including authors, titles, and abstracts. As you progress, you'll learn to perform similarity searches using vector embeddings.

Graduate project

This project covers the core topics of the Introduction to AI Engineering with Python course, making it sufficiently challenging to be a proud addition to your portfolio.

At least one graduate project is required to complete the course.

What you'll learn

Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:

Set up your local development environment and load the dataset.

Find the papers from specific categories (e.g., Artificial Intelligence) and explore their titles.

Find the number of papers published each year.

Find the least similar papers to "Continuity in Information Algebras" based on their vector embeddings.

Identify pairs of research papers authored by the same individuals or co-authors. Find all papers that share at least one common author and present them in a paired format.

Reviews

Sebastian Müller

3 months ago

4.3

I learned how to work with SQL joins, vector databases, and semantic search concepts. It was fun.

Dakouri Maurille-Constant Kobri

4 months ago

4.7

This project was an opportunity to work with PostgreSQL queries, vectory embedding with PostgreSQL pg-vector, and do some similarity search.

4.4

Learners who completed this project within the Introduction to AI Engineering with Python course rated it as follows:

Usefulness

4.5

Fun

4.2

Clarity

4.5