Week 3 Quiz - Hyperparameter tuning, Batch Normalization, Programming Frameworks(第三周测验 - 超参数调整，批量标 准化，编程框架)
\1. If searching among a large number of hyperparameters, you should try values in a grid rather than random values, so that you can carry out the search more systematically and not rely on chance. True or False? (如果在大量的超参数中搜索最佳的参数值，那么应该尝试在网格中搜索 而不是使用随机值，以便更系统的搜索，而不是依靠运气，请问这句话是正确的吗？)
【 】False (错误)
【 】True (正确)
答案
False
Note: Try random values, don’t do grid search. Because you don’t know which hyperparamerters are more important than others.And to take an extreme example, let’s say that hyperparameter two was that value epsilon that you have in the denominator of the Adam algorithm. So your choice of alpha matters a lot and your choice of epsilon hardly matters. (请注意：应当尝试随机值，不要使用网格搜 索，因为你不知道哪些超参数比其他的更重要。举一个很极端的例子，就比如在 Adam 算法中防止 除零操作的 ε 的值，一般为 1 的负 8 次方，但是和学习率 α 相比，ε 就显得不那么重要了。)
\2. Every hyperparameter, if set poorly, can have a huge negative impact on training, and so all hyperparameters are about equally important to tune well. True or False? (每个超参数如果设置得不好，都会对训练产生巨大的负面影响，因此所有的超参数都要调整好，请问这是正确的吗？)
【 】False (错误)
【 】True (正确) \
答案
False
Note:We’ve seen in lecture that some hyperparameters, such as the learning rate, are more critical than others. (注意：我们在视频中讲到的比如学习率这个超参数比其他的超参数更加重要。)
\3. During hyperparameter search, whether you try to babysit one model (“Panda” strategy) or train a lot of models in parallel (“Caviar”) is largely determined by: (在超参数搜索过程中，你 尝试只照顾一个模型（使用熊猫策略）还是一起训练大量的模型（鱼子酱策略）在很大程度 上取决于：)
【 】Whether you use batch or mini-batch optimization (是否使用批量（batch）或小批量优化 （mini-batch optimization）)
【 】The presence of local minima (and saddle points) in your neural network (神经网络中局部 最小值（鞍点）的存在性)
【 】The amount of computational power you can access (在你能力范围内，你能够拥有多大 的计算能力)
【 】 The number of hyperparameters you have to tune(需要调整的超参数的数量)
答案
【 】The amount of computational power you can access (在你能力范围内，你能够拥有多大 的计算能力)
\4. If you think