How to get from high school math to cutting-edge ML/AI: a detailed 4-stage roadmap with links to the best learning resources that I’m aware of. - Justin Skycak
justinmath.com · 4,196 words · saved by 5 readers
1) Foundational math. 2) Classical machine learning. 3) Deep learning. 4) Cutting-edge machine learning.
1) Foundational math. 2) Classical machine learning. 3) Deep learning. 4) Cutting-edge machine learning. Want to get notified about new posts? Join the mailing list and follow on X/Twitter. I recently talked to a number of people who work in software and want to get to the point where they can read serious ML/AI papers like Denoising Diffusion Probabilistic Models. But even though they did well in high school math, even AP Calculus, maybe even learned some undergraduate math… the math in these cutting-edge ML papers still looks like hieroglyphics. So, how do you get from high school…
saved by
related reading
- Math For Machine Learning [Resources]lelouch.dev
- Study Guide — LessWronglesswrong.com
- aman.ai • the art of artificial intelligenceaman.ai
- Justin Glibert - How I learned modern Machine Learningglibert.io
- Practical Deep Learning for Coders - Practical Deep Learningcourse.fast.ai
- Just know stuff. (Or, how to achieve success in a machine learning PhD.) · Patrick Kidgerkidger.site
- How I became a machine learning practitionerblog.gregbrockman.com
- GitHub - louisfb01/start-machine-learning: A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2026 without ANY background in the field and stay up-to-date with the latest news and state-of-the-argithub.com
- How to Generate a Specific, Actionable Upskilling Path in ANY Domainjustinmath.com
- GitHub - jacobhilton/deep_learning_curriculum: Language model alignment-focused deep learning curriculumgithub.com
- GenAI Handbookgenai-handbook.github.io
- Deep Learningdeeplearningbook.org