How to Keep Learning about Machine Learning
Eugene Yan
An essay outlines strategies for staying current with machine learning's rapid evolution, including experimenting with new tools in projects, completing personal projects that challenge you, attending meetups and conferences, reading papers consistently using a three-pass approach, finding mentors slightly ahead of your career stage, and maintaining a beginner's mindset. The author recommends attending one to two conferences annually and one meetup monthly, spending three to six months on personal projects, and conducting literature reviews before starting new work projects. By systematically learning through projects, community engagement, papers, and mentorship, practitioners can develop skills to solve previously intractable problems within one to two years.
Why it matters
Beyond getting that starting role, how does one continue growing in the field?
