Essential papers
Every practitioner should read these at least once:
- A Few Useful Things to Know about Machine Learning by Domingos
- Machine Learning that Matters by Wagstaff
- Tidy Data by Wickham
- Hidden Technical Debt by Sculley et al.
- The Unreasonable Effectiveness of Data by Halevy, Norvig, and Pereira
My decks & projects
- Build AI that matters — building AI that works under pressure, not just in demos
- A Tour of GenAI — where GenAI came from and where it’s headed
- Mapping Embeddings — from meaning to vectors and back
- If you want to work on AI for high-stakes domains, check out Awesome Safety-Critical AI
- I can’t edit them anymore, but some of my older articles are still available on AWS Community
For people in a hurry 🏃 💨
- Learn X in Y minutes delivers a whirlwind tour of (almost) every programming language — perfect for when you need to pick up a new language yesterday
- Icons and emojis for every occasion and mood
- Too lazy to implement that classic algorithm? The Algorithms has you covered with GitHub’s largest open-source algorithm library
- Hugging Face 🤗 Models lets you experiment with cutting-edge ML models online — completely free
- Need an API? This comprehensive list of publicly available APIs is your new best friend
- Stuck debugging? Invest in a rubber duck — yes, the bathtub kind works best
