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Ben Kuhn looks back on his past and realizes that since the beginning of his studies, he has tended to solve hard problems. Such a choice certainly works in school because it gets you higher grades, but not necessarily in the real world. So he suggests that in reality, we need to focus on important problems, not hard problems.
This is very similar to training machine learning models. In school, we only need to consider the metrics of the model itself when we publish a paper, and of course the higher the better. However, in industry it's completely different, because the model metrics and the business needs are not always aligned, and the metrics that measure how good the model is are not just the metrics of the model itself, but there may be some external metrics, such as processing speed and so on. So in industry, training a model requires a wider range of considerations, and choosing how to measure a model is more important than choosing what model to use.
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Ben Kuhn 回顾自己的过去，发现自读书开始，他就倾向于解决难题。这样的选择在学校里当然行得通，因为可以让你拿到更高的分数，但是在现实世界里却并不一定。所以他建议，在现实中，我们要着眼于重要问题，而不是难题。
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