LLM Next Level: Chat with PDFs (Using Vision)

LLM Next Level: Chat with PDFs (Using Vision)

Combine Computer Vision and Large Language Models (LLMs) to extract and analyze information from PDFs. #digital

By ETH Student Project House

Select date and time

Tuesday, April 30 · 4:30 - 6pm CEST

Location

SPH - FHK - H Floor

Clausiusstrasse 16 8006 Zürich Switzerland

About this event

Building on the ML intro Workshop, you will learn how Large Language Models (LLMs) work, how you can use them, and what their limitations are. Explore the fusion of Computer Vision techniques with LLMs to extract structured information from PDF files effectively.

Through hands-on exercises, participants will learn to develop a comprehensive program capable of analyzing PDF documents, extracting relevant data, and providing insightful answers to specific inquiries.Our goal is to give an all-in-one program that analyze your PDFs and answer specific questions about them.

Prerequisites

  • No coding expertise in a specific language is required but a basic understanding of programation will help.
  • Bring your own laptop.
  • Open to all ETH students and Student Project House community members.
  • The Machine Learning and LLM Intro 1 Workshop is NOT a prerequisite

About the Digital Makerspace

The Digital Makerspace is a place to grow and build your digital project within SPH.

The Digital Makerspace Managers are passionate group of makers, programmers and hackers (in the original word sense!). We love to build digital stuff and find creative solutions. We are looking forward getting to know you and build something together.

Frequently asked questions

Do I need to install something before the event?

No, it's not needed to install something on your laptop. We will use Google colab. Everything will run in the cloud, and you can access it with your browser. So the only thing you need is your laptop and a browser.

Do I need to know Python or any other programming language?

No, you don't need to know Python or any other programming language. We will look at some python code, but we are starting from a working solution. So it's very easy to start and to make changes. We will also go over the code line by line, so you will understand what it means.

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