吴恩达-深度学习入门-第一周课后测验题

一、文章简介

本篇文章主要内容为第一周课程结束后的十几道测试题，在其他大佬的文章中看到题目后，感觉因为有答案所以不太利于自己的思考，所以进行一下简单的编辑工作，将答案放在文章最后。
文章参考: https://blog.csdn.net/u013733326/article/details/79862336
仅做学习使用。

二、题目

题目分为中文版和英文版，根据自己喜好选择观看即可。
英文版：

Week 1 Quiz - Introduction to deep learning

1、What does the analogy “AI is the new electricity” refer to?

[ ]AI is powering personal devices in our homes and offices, similar to electricity.
[ ]Through the “smart grid”, AI is delivering a new wave of electricity.
[ ]AI runs on computers and is thus powered by electricity, but it is letting computers do things not possible before.
[ ]Similar to electricity starting about 100 years ago, AI is transforming multiple industries.
Note: Andrew illustrated the same idea in the lecture.

2、Which of these are reasons for Deep Learning recently taking off? (Check the two options that apply.)

[ ] We have access to a lot more computational power.
[ ]Neural Networks are a brand new field.
[ ] We have access to a lot more data.
[ ]Deep learning has resulted in significant improvements in important applications such as online advertising, speech recognition, and image recognition.

3、Recall this diagram of iterating over different ML ideas. Which of the statements below are true? (Check all that apply.)

[ ] Being able to try out ideas quickly allows deep learning engineers to iterate more quickly.
[ ] Faster computation can help speed up how long a team takes to iterate to a good idea.
[ ]It is faster to train on a big dataset than a small dataset.
[ ] Recent progress in deep learning algorithms has allowed us to train good models faster (even without changing the CPU/GPU hardware).

4、When an experienced deep learning engineer works on a new problem, they can usually use insight from previous problems to train a good model on the first try, without needing to iterate multiple times through different models. True/False?

[ ]True
[ ] False

5、Which one of these plots represents a ReLU activation function?

Note: Did not get the picture, had to put a correct picture.

6、Images for cat recognition is an example of “structured” data, because it is represented as a structured array in a computer. True/False?

[ ]True
[ ] False

7、A demographic dataset with statistics on different cities’ population, GDP per capita, economic growth is an example of “unstructured” data because it contains data coming from different sources. True/False?

[ ]True
[ ] False

8、Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French? (Check all that apply.)

[ ] It can be trained as a supervised learning problem.
[ ]It is strictly more powerful than a Convolutional Neural Network (CNN).
[ ] It is applicable when the input/output is a sequence (e.g., a sequence of words).
[ ]RNNs represent the recurrent process of Idea->Code->Experiment->Idea->….

9、In this diagram which we hand-drew in lecture, what do the horizontal axis (x-axis) and vertical axis (y-axis) represent?

10、Assuming the trends described in the previous question’s figure are accurate (and hoping you got the axis labels right), which of the following are true? (Check all that apply.)

[ ] Increasing the training set size generally does not hurt an algorithmic performance, and it may help significantly.
[ ] Increasing the size of a neural network generally does not hurt an algorithmic performance, and it may help significantly.
[ ]Decreasing the training set size generally does not hurt an algorithmic performance, and it may help significantly.
[ ]Decreasing the size of a neural network generally does not hurt an algorithmic performance, and it may help significantly.

中文版：

1、和“AI是新电力”相类似的说法是什么？

【 】AI为我们的家庭和办公室的个人设备供电，类似于电力。
【 】通过“智能电网”，AI提供新的电能。
【 】AI在计算机上运行，​​并由电力驱动，但是它正在让以前的计算机不能做的事情变为可能。
【 】就像100年前产生电能一样，AI正在改变很多的行业。
请注意: 吴恩达在视频中表达了同样的观点。

2、哪些是深度学习快速发展的原因？ (两个选项)

【 】现在我们有了更好更快的计算能力。
【 】神经网络是一个全新的领域。
【 】我们现在可以获得更多的数据。
【 】深度学习已经取得了重大的进展，比如在在线广告、语音识别和图像识别方面有了很多的应用。

3、回想一下关于不同的机器学习思想的迭代图。下面哪(个/些)陈述是正确的？

【 】能够让深度学习工程师快速地实现自己的想法。
【 】在更好更快的计算机上能够帮助一个团队减少迭代(训练)的时间。
【 】在数据量很多的数据集上训练上的时间要快于小数据集。
【 】使用更新的深度学习算法可以使我们能够更快地训练好模型（即使更换CPU / GPU硬件）。

4、当一个经验丰富的深度学习工程师在处理一个新的问题的时候，他们通常可以利用先前的经验来在第一次尝试中训练一个表现很好的模型，而不需要通过不同的模型迭代多次从而选择一个较好的模型，这个说法是正确的吗？

【 】正确
【 】错误

5、这些图中的哪一个表示ReLU激活功能？

博主注：没有搞到图，只好放一张正确的图。

6、用于识别猫的图像是“结构化”数据的一个例子，因为它在计算机中被表示为结构化矩阵，是真的吗？

【 】正确
【 】错误

7、统计不同城市人口、人均GDP、经济增长的人口统计数据集是“非结构化”数据的一个例子，因为它包含来自不同来源的数据，是真的吗？

【 】正确
【 】 错误

8、为什么在上RNN（循环神经网络）可以应用机器翻译将英语翻译成法语？

【 】因为它可以被用做监督学习。
【 】严格意义上它比卷积神经网络（CNN）效果更好。
【 】它比较适合用于当输入/输出是一个序列的时候（例如：一个单词序列）
【 】RNNs代表递归过程：想法->编码->实验->想法->…

9、在我们手绘的这张图中，横轴（x轴）和纵轴（y轴）代表什么?

10、假设上一个问题图中描述的是准确的（并且希望您的轴标签正确），以下哪一项是正确的?

【 】 增加训练集的大小通常不会影响算法的性能，这可能会有很大的帮助。
【 】增加神经网络的大小通常不会影响算法的性能，这可能会有很大的帮助。
【 】减小训练集的大小通常不会影响算法的性能，这可能会有很大的帮助。
【 】减小神经网络的大小通常不会影响算法的性能，这可能会有很大的帮助。

三、答案

1、

[x]Similar to electricity starting about 100 years ago, AI is transforming multiple industries.
Note: Andrew illustrated the same idea in the lecture.

【★】就像100年前产生电能一样，AI正在改变很多的行业。

2、

[x] We have access to a lot more computational power.
[x] We have access to a lot more data.

【★】 现在我们有了更好更快的计算能力。
【★】 我们现在可以获得更多的数据。

3、

[x] Being able to try out ideas quickly allows deep learning engineers to iterate more quickly.
[x] Faster computation can help speed up how long a team takes to iterate to a good idea.
[x] Recent progress in deep learning algorithms has allowed us to train good models faster (even without changing the CPU/GPU hardware).
Note: A bigger dataset generally requires more time to train on a same model.

【★】能够让深度学习工程师快速地实现自己的想法。
【★】在更好更快的计算机上能够帮助一个团队减少迭代(训练)的时间。
【★】 使用更新的深度学习算法可以使我们能够更快地训练好模型（即使更换CPU / GPU硬件）。
请注意: 同一模型在较大的数据集上通常需要花费更多时间。

4、

[x] False
Note: Maybe some experience may help, but nobody can always find the best model or hyper-parameters without iterations.

【★】 错误
请注意：也许之前的一些经验可能会有所帮助，但没有人总是可以找到最佳模型或超参数而无需迭代多次。

5、

正确图片已经放在题目下面了

6、

[x] False

【★】 错误
博主注：图片属于非结构化数据。

7、

[x] False

【★】 错误
博主注：单纯的看以上数据的话就是非结构化数据，但是这些数据都被整合到了数据集里面，所以是结构化数据。

8、

[x] It can be trained as a supervised learning problem.
[x] It is applicable when the input/output is a sequence (e.g., a sequence of words).

【★】 因为它可以被用做监督学习。
【★】 它比较适合用于当输入/输出是一个序列的时候（例如：一个单词序列）

9、

x-axis is the amount of data
y-axis (vertical axis) is the performance of the algorithm.

【★】x轴是数据量
【★】y轴（垂直轴）是算法的性能（博主注：也可以说是准确率）

10、

[x] Increasing the training set size generally does not hurt an algorithm鈥檚 performance, and it may help significantly.
[x] Increasing the size of a neural network generally does not hurt an algorithm鈥檚 performance, and it may help significantly.

【★】 增加训练集的大小通常不会影响算法的性能，这可能会有很大的帮助。
【★】 增加神经网络的大小通常不会影响算法的性能，这可能会有很大的帮助。