Lilly Tech Systems
How to Read AI Papers
Develop the skills to read, understand, and implement academic AI research papers — from navigating arXiv to understanding math notation and reproducing results.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Why read papers, how research drives AI progress, and a practical reading strategy for practitioners.
2. Paper Structure
Abstract, introduction, related work, methods, experiments, results, and conclusion — what each section tells you.
3. arXiv Navigation
Find papers on arXiv, use Semantic Scholar, track citations, follow researchers, and stay current.
4. Key Papers
Walkthrough of "Attention Is All You Need" and other landmark papers that shaped modern AI.
5. Implementing Papers
Turn paper descriptions into working code: understanding math notation, pseudocode, and reproducing results.
6. Best Practices
Reading habits, note-taking systems, paper discussion groups, and building a research reading practice.
What You'll Learn
By the end of this course, you'll be able to:
Read Papers Efficiently
Use the three-pass method to quickly assess relevance, understand key contributions, and extract details.
Find Relevant Research
Navigate arXiv, Google Scholar, and Semantic Scholar to discover papers relevant to your work.
Understand Math Notation
Decode common mathematical notation used in ML papers: summations, matrices, probability, and loss functions.
Implement Papers
Translate paper descriptions and equations into working PyTorch or TensorFlow code and reproduce key results.