原文链接：https://blog.csdn.net/u013733326/article/details/79862336
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.
解：4
AI是新的电能，作者认为是因为AI带来了类似电能一样的变革。
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.
解：1 3
深度学习近些年来快速发展的原因有高计算力和大数据量两点。
2：神经网络不是一个全新的领域，也称联结主义，有着几十年的历史。
4：深度学习有很多应用不是快速发展的原因，是快速发展的结果。
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).
解：1 2 4
3：在使用同一个模型的前提下，训练数据量越大，训练需要的时间也就越多。
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
解：2
在模型选择时，是不能直接通过直觉进行直接选择，直觉可能会有所帮助，但是使用不同的模型迭代是非常有必要的。
5.Which one of these plots represents a ReLU activation function?
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
解：2
图片属于非结构化数据。
7.A demographic dataset with statistics on different cities’ population, GDP per capita, economic growth is an example of “unstructured” data because it contains data coming from different sources. True/False?
- True
- False
解：2
不同城市人口、人均GDP、经济增长的人口统计数据集是结构化的数据。结构化数据与非结构化数据的区别不是因为数据来源。
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->….
解：1 3
因为机器翻译的数据具有时序性的特点，而且是一个有监督学习。
2：不同的神经网络适用范围不同，没有好坏之分。
4：这个递归思想是神经网络训练的思想，不止适用于RNN。
9.In this diagram which we hand-drew in lecture, what do the horizontal axis (x-axis) and vertical axis (y-axis) represent?
x-axis is the amount of data
y-axis (vertical axis) is the performance of the algorithm.
解：X轴代表数据量，Y轴代表算法的性能。
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 algorithm performance, and it may help significantly.
- Increasing the size of a neural network generally does not hurt an algorithm performance, and it may help significantly.
- Decreasing the training set size generally does not hurt an algorithm performance, and it may help significantly.
- Decreasing the size of a neural network generally does not hurt an algorithm performance, and it may help significantly.
解：1 2
模型训练时，增加训练集大小和神经网络的大小通常不会影响算法的性能（后面的平坡），而且可能会提高算法的性能（上坡）。