神经网络与深度学习（3）

0.写在前面

下面到了我们的神经网络与深度学习课程的作业环节了，由于在国内，所以有很多图片加载不出来，我只能臆想了。（这种简单的还好说，等到后面难的部分，我可想不出来。）
再次感谢网友的建议，根据coursera 荣誉准则不允许公布答案，因此我将答案隐去了。

1.习题1

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

A. AI is powering personal devices in our homes and offices, similar to electricity.

B. Through the “smart grid”, AI is delivering a new wave of electricity.

C. Similar to electricity starting about 100 years ago, AI is transforming multiple industries.

D. AI runs on computers and is thus powered by electricity, but it is letting computers do things not possible before.

参考答案：coursera 荣誉准则不允许公布答案
解析：AI是一种新的生产力，就像100年前的电一样。也和200年前的蒸汽一样。

2

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

A. Neural Networks are a brand new field.

B. Deep learning has resulted in significant improvements in important applications such as online advertising, speech recognition, and image recognition.

C. We have access to a lot more data.

D. We have access to a lot more computational power.

参考答案: coursera 荣誉准则不允许公布答案
解析：第一节讲的，深度学习的成功应用是流行的一个原因，第二节讲的，数据、计算力和算法的突破是流行的根本原因。

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 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).

参考答案：coursera 荣誉准则不允许公布答案
解析：迭代的过程就是我们在上一节讲的。

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?

A. True

B. False

参考答案：coursera 荣誉准则不允许公布答案
解析：一个有经验的深度学习工程师可以很快的移植原有的模型到新的问题上，因为有监督的深度学习不需要特征工程，只需要给出X和y即可。

5

Which one of these plots represents a ReLU activation function?

参考答案：

解析：Relu函数是修正线性单元，大于0的部分导数为1，小于0的部分为0。

6

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

A. True

B. False

参考答案：coursera 荣誉准则不允许公布答案
解析：声音，文字，图片都是非结构化数据。

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?

A. True

B. False

参考答案：coursera 荣誉准则不允许公布答案
解析：这种是结构化的数据，类似表格一样。

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.g., a sequence of words).

D. RNNs represent the recurrent process of Idea->Code->Experiment->Idea->….

参考答案：coursera 荣誉准则不允许公布答案
解析：RNN适合机器翻译，主要是在于机器翻译可以被看作是一个监督学习的项目，并且RNN比CNN更适合这种序列化的问题。

9

In this diagram which we hand-drew 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.

参考答案： coursera 荣誉准则不允许公布答案
解析：暂且认为是课程里提供的那张图吧。

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. Decreasing 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 training set size generally does not hurt an algorithm’s performance, and it may help significantly.

D. Increasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly.

参考答案：coursera 荣誉准则不允许公布答案
解析：增加训练集和网络结构不会伤害一个算法的性能体现，而且有时候还有帮助。