Large Language Models for R Users

Author

Emi Tanaka

Published

March 19, 2025

Optimal viewing experience

Please note that this website is best viewed using desktop or laptop using modern browsers, e.g. Google Chrome.

👋 Welcome

This livestream introduces the fundamental concepts of large language models (LLMs), including its architecture (tokenization, embedding, neural network, and transformer model), its general training process and its usage with prompt engineering and parameter selection. Practical applications will be demonstrated using OpenAI LLMs (now expanded to also Google Gemini models and Anthropic Claude models) and local Ollama models through R. Participants will have access to all the slides and code used in the demonstration, so you can replicate the analyses and continue exploring LLMs on your own machines. The session will include interactive Q&A via Zoom chat, with live responses and email follow-ups for curated unanswered questions.

If you would like to follow along with the code, click on the Preparation button below for instructions.

Preparation

🕜 Schedule

Note

The schedule is a rough guideline and may change slightly during the livestream.

Perth Brisbane Adelaide AEDT Content
09:45am 11:45am 12:15pm 12:45pm Getting Started
09:55am 11:55am 12:25pm 12:55pm Landscape of Large Language Models
10:55am 12:55pm 01:25pm 01:55pm Break
11:10am 01:10pm 01:40pm 02:10pm Parameters of Large Language Models
11:40am 01:40pm 02:10pm 02:40pm Break
11:55am 01:55pm 02:25pm 02:55pm Prompt Engineering
12:55pm 02:55pm 03:25pm 03:55pm Break
01:10pm 03:10pm 03:40pm 04:10pm Architecture of Large Language Models
01:50pm 03:50pm 04:20pm 04:50pm Next steps
  • AEDT includes Sydney, Melbourne and Canberra
  • There will be a Q&A at the start and end of each session.

📚 Slides

️ Demos

License

These workshop materials by Emi Tanaka is licensed under CC BY-NC-ND 4.0 – which means you can share the link with your colleagues, peers, friends, family and so on, so long as due credit is given to the author. However, you cannot commercialise or create derivatives from this work without the permission of the author (emi.tanaka@anu.edu.au).