Getting Ready for the Workshop

Curated materials to help you prepare and get the most out of your workshop experience

Welcome! We've carefully selected a collection of high-quality learning materials to help you prepare for the SCIPE Workshop on LLMs. Whether you're new to the field or looking to deepen your understanding, these resources will provide a solid foundation for the workshop.

Take your time exploring these materials at your own pace. You don't need to complete everything before the workshop, but familiarizing yourself with the basics will help you get more value from the hands-on sessions.

Introduction to Large Language Models

Start here to build a strong foundation in LLMs

How I Use LLMs

Andrej Karpathy

Practical insights on leveraging LLMs in real-world workflows. Learn how an expert integrates these tools into daily work and discovers what's possible with current technology.

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Large Language Models and Chatbots

IBM Technology

A comprehensive playlist that covers LLM fundamentals and chatbot development. These videos break down complex topics into digestible segments, perfect for building understanding step by step.

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Neural Networks

3Blue1Brown

Beautiful visual explanations of neural network concepts. If you're looking to understand the mathematical foundations with intuitive animations, this series is unmatched. Essential for grasping how neural networks actually work.

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Learn RAG From Scratch

LangChain Engineer

Python AI tutorial on building retrieval-augmented generation systems. This practical guide shows you how to enhance LLMs with external knowledge, a technique we'll explore in the workshop.

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Advanced

Let's Reproduce GPT-2 (124M)

Andrej Karpathy

An in-depth walkthrough of implementing GPT-2 from scratch. This is for those who want to understand the intricate details of transformer architecture and training. Consider this optional but highly rewarding for research-oriented participants.

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University Courses

Structured learning paths from leading universities

Large Language Models Courses

Transformers & LLMs

Stanford University - CME 295, Fall 2025

A comprehensive course dedicated to transformer architectures and large language models. Covers both theoretical foundations and practical implementations, ideal for understanding the complete picture of modern LLMs.

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Advanced Natural Language Processing

Carnegie Mellon University, Fall 2025

Dive deeper into NLP with this advanced course. Features video lectures covering cutting-edge topics in natural language processing. Great for those who want to explore beyond the basics.

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Inference Algorithms for Language Modeling

Carnegie Mellon University, Fall 2025

Focused course on inference optimization techniques. Perfect for understanding how to make LLMs faster and more efficient, a topic we'll cover extensively in Day 2 of the workshop.

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Machine Learning Foundations

Deep Learning

Stanford University - CS 230, Fall 2025

Build a strong foundation in deep learning concepts and applications. This course covers the fundamentals that underpin all modern LLMs. Recommended if you're new to deep learning.

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How to Prepare

For Beginners

Priority: Start with Andrej Karpathy's 1-hour introduction, then watch the 3Blue1Brown neural networks series. These two resources will give you a solid conceptual foundation.

Optional: Browse the IBM Technology playlist for specific topics that interest you.

For Practitioners

Priority: Review "How I Use LLMs" and the RAG tutorial to see practical applications. Look at the Stanford CME 295 syllabus to identify areas where you want to deepen your knowledge.

Optional: Explore the inference optimization course if you're interested in deployment efficiency.

For Researchers

Priority: Review the advanced NLP course and inference algorithms course. Consider watching the GPT-2 reproduction video to understand implementation details.

Optional: Dive into the CS 230 materials if you want to refresh your deep learning fundamentals.

Timeline Suggestion

6+ weeks before: Start with the foundational videos and courses that interest you most.

2-4 weeks before: Focus on areas relevant to your goals (practitioner tools or research topics).

1 week before: Review key concepts and make sure you have your development environment set up.

Remember: These are suggestions to enhance your workshop experience, not requirements. Come with curiosity and a willingness to learn!

Ready to Join Us?

Registration is open from November 13 to December 13, 2025

Register for the Workshop

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