10,000-hour rule as a Metaphor

“Beginners are often focused on what to do and I think the focus should be more like how much you do. You just have to pick the things where you can spend time, you care about, and you're interested in. You literally have to put in 10,000 hours of work. You'll iterate, you'll improve, and you'll accumulate scar tissue and build good intuition” — Advice for machine learning beginners

Whenever I hear about the 10,000-hour rule, I think about these things

• It's a metaphor for deep, hard work
• It's not about clocking in hours. It's about pushing yourself to practice deliberately towards a goal, so the quality of work is equally important
• My answer to quality vs quantity is: optimize for both. They come in handy when using both as tools. Sometimes you conduct with quantity, allowing repetition to sharpen your skills. Other times, you slow down and ensure quality takes place and tackle the right problem, the right challenge, with the right solution.
• Quantity: iteration is really important. The act of doing, learning, receiving feedback, refining, making it better, and doing it again. Also important because it allows you not to get caught up in the paralysis of perfection
• Quality: deliberately fix the right problems, fill in the (knowledge) gaps, push yourself to learn new and advanced topics, and don't let yourself keep in your comfort zone.
• What to work on is important but sometimes debating resource X or resource Y doesn't really matter. What matters is putting a lot of work into it. Putting the 10.000 hours of work. Until you get really good at it.

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