For most people, this is advice that's hard to follow - they desperately want to prove themselves right. The most effective way to do this is usually to try to prove yourself wrong. There's a template you can use.īetter experiment design: You want to come up with an experiment that will maximize how much you learn. No matter what context you find yourself in - science, programming, product design, etc. And since you only have limited time available, that means you need to remove bottlenecks along the loop so you can move through it faster.

In order to get better ideas, create better products, publish better papers, you need to run more iterations through the loop of progress.

Ponder the data generated by your experiment, and update your idea. Run a test to evaluate the quality of your idea. Come up with a new idea - or an improvement on an existing idea. Iterating on your ideas is a feedback loop, where the results of each cycle inform the next one. They kept pivoting and adjusting at a fast pace before becoming an 'overnight' success.Ī version of this is even true for people: the more willing we are to reevaluate our views and admit mistakes, the faster we learn and the further we can improve. The best startups didn't start out with product-market-fit on day one. The people who win machine learning competitions on Kaggle aren't those that started out with the cleverest approach, they're the ones who iterated on their approach more times, guided by a reliable evaluation process. They're the ones who tweaked their theories further than others, in close contact with experimental reality. The most effective scientists aren't necessarily the smartest ones. They're born from years of iterative refinement.Įverywhere you look, you see the same pattern. The best ideas rarely spring from a sudden 'Eureka' moment or from an ambitious brainstorming session. You develop interesting ideas not by being clever, but by thinking about things for a very long time, with obsessive relentlessness - by following every trail of thought, opening door after door, and not stopping until you reach a conclusion.

Deep learning by francois chollet