(1) What does the analogy “AI is the new electricity” refer to?
[A] AI runs on computers and is thus powered by electricity, but it is letting computers do things not possible before.
[B]AI is powering personal devices in our homes and offices, similar to electricity.
[C]Through the “smart grid”, AI is delivering a new wave of electricity.
[D] Similar to electricity starting about 100 years ago, AI is transforming multiple industries.
答案：D
解析：Started about 100 years ago, electricity had once transformed countless industries: transportation, manufacturing, healthcare, communications, and more.
AI will now bring about an equally big transformation.
(2)Which of these are reasons for Deep Learning recently taking off?(Check the two options that apply).
[A]Neural Networks are a brand new field.
[B]We have access to a lot more data.
[C]We have access to a lot more computational power.
[D]Deep learning has resulted in significant improvements in important applications such as online advertising, speech recognition, and image recognition.
答案：B，C
解析：Deep learning is taking off due to a large amount of data available through the digitization of the society, faster computation and innovation in the development of neural network algorithm.
(3)Recall this diagram of iterating over different ML ideas. Which of the statements below are true?(Check all that apply)
[A]Being able to try out ideas quickly allows deep learning engineers to iterate more quickly.
[B]Faster computation can help speed up how long a team takes to iterate to a good idea.
[C]It is faster to train ton a big dataset than s small dataset.
[D]Recent progress in deep learning algorithms has allowed us to train good models faster(even without changing the CPU/GPU hardware)
答案：A,B,D
解析：训练集越大训练速度越慢
(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?
答案：False
解析： 可能之前的经验有帮助，但几乎没有人可以在第一次训练的情况下就获得一个最佳模型
(5)Which one of these plots re[resents a ReLU activation function?
[A]
[B]
[C]
[D]
答案：C
(6)Images for cat recognition is an example of “structured” data, because it is represented as a structured array in a computer. True/False?
答案：False
解析：音频、图像这类属于非结构化数据
(7)A demographic dataset with statistics on different cities’ population, GDP per capita economic growth is an example of “unstructured” data because it contaions data coming from different sources. True/False?
答案：False
解析：是否是结构化数据与数据来源无关
(8)Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French?(Check all that apply)
[A]It can be trained as a supervised learning problem.
[B]It is strictly more powerful than a Convolutional Neural Network(CNN).
[C]It is applicable when the input/output is a sequence(e.e.,a sequence of words).
[D]RNNs represent the recurrent process of Idea->Code->Experiment->Idea->…
答案：A,C
解析：对于B选项，在图像领域中使用CNN更合适，在对于序列数据时，使用RNN更合适。
对于D选项，对于RNN的细节，具体见后续课程
(9)In this diagram which we hand-draw in lecture, what do the horizontal axis(x-axis) and vertical axis(y-axis) represent?
[A]x-axis is the amount of data, y-axis is the size of the model you train.
[B]x-axis is the input to the algorithm, y-axis is outputs.
[C]x-axis is the amount of data,y-axis(vertical axis) is the performance of the algorithm.
[D]x-axis is the performance of the algorithm, y-axis(vertical axis) is the amount of data.
答案：C
(10)Assuming the trends described in the previous question’s figure are accurate (and hoping you got the axis labels right), which of the follow are true?(check all that apply)
[A]Increasing the training set size generally does not hurt an algorithm\s performance, and it may help significantly.
[B]Decreasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly.
[C]Increasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly.
[D]Decreasing the training set size generally does not hurt an algorithm’s performance, and it may help significantly.
答案：A,B
解析：增加数据集的大小和网络的大小不会影响算法的性能，并且可能对算法的性能有显著的帮助