课程一(Neural Networks and Deep Learning)，第一周（Introduction to Deep Learning）—— 2、10个测验题
1、What does the analogy “AI is the new electricity” refer to? (B)
A. Through the “smart grid”, AI is delivering a new wave of electricity.
B. Similar to electricity starting about 100 years ago, AI is transforming multiple industries.
C. AI is powering personal devices in our homes and offices, similar to electricity.
D. AI runs on computers and is thus powered by electricity, but it is letting computers do things not possible before.
2、Which of these are reasons for Deep Learning recently taking off? (Check the three options that apply.) (A、B、D)
A. We have access to a lot more data.
B. We have access to a lot more computational power.
C. Neural Networks are a brand new field.
D. Deep learning has resulted in significant improvements in important applications such as online advertising, speech recognition, and image recognition.
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 on a big dataset than a small dataset.
D. 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? (B)
A. True
B. False
5、Which one of these plots represents a ReLU activation function? (C)
A. Figure 1:
B. Figure 2:
C. Figure 3:
D.Figure4
6.Images for cat recognition is an example of “structured” data, because it is represented as a structured array in a computer. True/False? (B)
A. True
B. 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?(B)
A. True
B. False
8.Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French? (Check all that apply.) (A、C)
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.g., a sequence of words).
D. 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? (A)
A.
x-axis is the amount of data
y-axis (vertical axis) is the performance of the algorithm.
B.
x-axis is the performance of the algorithm
y-axis (vertical axis) is the amount of data.
C.
x-axis is the amount of data
y-axis is the size of the model you train.
D.
x-axis is the input to the algorithm
y-axis is outputs.
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.) (A、C)
A. Increasing the size of a neural network 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 training set size 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.
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A. Figure 1:
B. Figure 2:
C. Figure 3:
D.Figure4
7、
A、真
B、假
8、为什么 RNN (递归神经网络) 用于机器翻译, 说将英语翻译成法语？(检查所有适用的）（A、C）
A、它可以被训练作为一个被监督的学习问题。
B、它是严格比卷积神经网络 (CNN) 更强大。
C、当输入/输出是一个序列 (例如, 一个单词序列) 时, 它是适用的。
D、RNNs 代表了思想的反复过程->> 代码->> 实验->> 想法...。
9、
A、
B、
C、
D、