# 1.1 为什么是 ML 策略？（Why ML Strategy?）

当最初得到一个深度神经网络模型时，希望从很多方面来对它进行优化，例如：

* **Collect more data**
* **Collect more diverse training set**
* **Train algorithm longer with gradient descent**
* **Try Adam instead of gradient descent**
* **Try bigger network**
* **Try smaller network**
* **Try dropout**
* **Add L2 regularization**
* **Network architecture: Activation functions, #hidden units…**

可选择的方法很多、很复杂、繁琐。盲目选择、尝试不仅耗费时间而且可能收效甚微。因此，使用快速、有效的策略来优化机器学习模型是非常必要的。


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