深度學習優化策略/經驗

策略1：Choose dev and test sets to reflect data you expect to get in the future and want to do well on.（比如驗證集dev和測試集test都用我們可能想要測試的一些圖片，人臉可以用現實生活中拍到的人臉做dev和test）

策略1中可能遇到的問題：There is a chance that your team will build something that works well on the dev set, only to find that it does poorly on the test set.可運用策略2。

策略2.1：suppose your team develops a system that works well on the dev set but not the test set. If your dev and test sets had come from the same distribution, then you would have a very clear diagnosis of what went wrong: You have overfit the dev set. The obvious cure is to get more dev set data.（dev和test來自於同一分佈distribution，我想應該就是場景類似的意思）

策略2.2：It is an important research problem to develop learning algorithms that are trained on one distribution and generalize well to another. But if your goal is to make progress on a specific machine learning application rather than make research progress, I recommend trying to choose dev and test sets that are drawn from the same distribution

以上是前6節我說看到有用的