神经网络与深度学习第一周测验 Introduction to Deep Learning

Posted 2021-07-07 沧夜2021

第一周测验 Introduction to Deep Learning

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

AI是新的电力指的是？

• AI runs on computers and is thus powered by electricity, but it is letting computers do things not possible before.
  AI 在计算机上运行，由电力供电，但它让计算机以前不可能做事。
• Through the “smart grid”, AI is delivering a new wave of electricity.
  通过"智能电网"，人工智能正在提供新一轮的电力。
• AI is powering personal devices in our homes and offices, similar to electricity.
  AI 正在为家庭和办公室的个人设备供电，类似于电力
• Similar to electricity starting about 100 years ago, AI is transforming multiple industries.
  与大约 100 年前开始的电力类似，人工智能正在改变多个行业。

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

深度学习迅速发展的原因？

• Deep learning has resulted in significant improvements in important applications such as online advertising, speech recognition, and image recognition.
  深度学习是的在线广告，语言识别，图像识别等重要应用有了显著改进
• We have access to a lot more computational power.
  可以获得更多的算力
• Neural Networks are a brand new field.
  神经网络是新的领域
  神经网络90年代就出来理论了
• We have access to a lot more data.
  可以获得更多的数据

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

回想这个图，以下哪个说法是对的？

• It is faster to train on a big dataset than a small dataset.
  在大数据集中进行训练比在小数据集上训练更快。
  数据规模越大训练越慢
• Faster computation can help speed up how long a team takes to iterate to a good idea.
  更快的计算可以加快团队迭代到好方式所需的时间。
• Being able to try out ideas quickly allows deep learning engineers to iterate more quickly.
  能够快速尝试想法，使深度学习的工程师能够更快地迭代。
• Recent progress in deep learning algorithms has allowed us to train good models faster (even without changing the CPU/GPU hardware).
  最近在深度学习算法方面的进步使我们能够更快地训练好模型（即使无需更改 CPU/GPU 硬件

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?

当一个有经验的深度学习工程师在解决一个新问题时，他们通常可以使用以前的经验一次就训练一个好的模型，而无需通过不同的模型多次重复。真/假？
还是需要对模型进行调整拟合，一招鲜吃遍天基本不管用

• False
• True

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

其中哪一个图表示ReLU激活函数

• 

ReLU函数

• 

sigmoid函数

• 

leaky ReLU函数

• 

tanh函数

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

猫的识别图像是"结构化"数据的示例，是因为它在计算机中表示为结构化数组。真/假？
图像，语音，文本都不是结构化数据

• False
• True

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?

人口数据集包含不同城市的人口统计数据、人均 GDP、经济增长等。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。真/假？
这些数据可以在数据库中表示出来，应该是结构化数据。是不是结构化数据跟来源无关

• False
• True

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

为什么 RNN（卷积神经网络）用于机器翻译，比如将英语翻译成法语？

• It is applicable when the input/output is a sequence (e.g., a sequence of words).
  当输入/输出是一个序列（例如，单词序列）时，它适用
• It can be trained as a supervised learning problem.
  它可以被训练成一个有监督的学习问题。
• RNNs represent the recurrent process of Idea->Code->Experiment->Idea->…
  RNN 代表想法 - >代码 - >体验 - >意>的反复过程。
• It is strictly more powerful than a Convolutional Neural Network (CNN).
  它比卷积神经网络 （CNN） 更强大。

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

在我们讲课时亲手绘制的这张图表中，水平轴（x轴）和垂直轴（y轴）代表什么？

• 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
• x-axis is the input to the algorithm
  y-axis is outputs.
  x轴是算法的输入
  y轴是输出。
• x-axis is the amount of data
  y-axis is the size of the model you train.
  x轴是数据量
  y轴是您训练的模型的大小。
• x-axis is the performance of the algorithm
  y-axis (vertical axis) is the amount of data.
  x轴是算法的性能
  y 轴（垂直轴）是数据量。

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.)

假设上一个问题的数字中描述的趋势是准确的（并希望您得到正确的轴标签），以下哪一个是正确的？

• Increasing the training set size generally does not hurt an algorithm’s performance, and it may help significantly.
  增加培训集大小通常不会损害算法的性能，并且可能会有显著帮助。
• Decreasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly.
  缩小神经网络的大小通常不会损害算法的性能，而且可能会有显著帮助。
• Increasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly.
  增加神经网络的大小通常不会损害算法的性能，而且可能会有显著帮助
• Decreasing the training set size generally does not hurt an algorithm’s performance, and it may help significantly.
  降低培训集大小通常不会损害算法的性能，并且可能会有显著帮助。