深度学习第一周Introduction to Deep Learning习题整理

Introduction to Deep Learning

1. Which of the following best describes the role of AI in the expression “an AI-powered society”?

• Similar to electricity starting about 100 years ago, AI is transforming multiple industries.
• 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.
• AI is powering personal devices in our homes and offices, similar to electricity.

2. Which of the following are reasons that didn’t allow Deep Learning to be developed during the '80s?

• The theoretical tools didn’t exist during the 80’s.
• Limited computational power.
• People were afraid of a machine rebellion.
• Interesting applications such as image recognition require large amounts of data that were not available.

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

• It is faster to train on a big dataset than a small dataset.
• 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.
• Recent progress in deep learning algorithms has allowed us to train good models faster (even without changing the CPU/GPU hardware).

4. Neural networks are good at figuring out functions relating an input xx to an output yy given enough examples. True/False?

• False
• True

5. ReLU stands for which of the following?

• Representation Linear Unit
• Rectified Last Unit
• Rectified Linear Unit
• Recognition Linear Unit

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

7. A dataset is composed of age and weight data for several people. This dataset is an example of “structured” data because it is represented as an array in a computer. True/False?

• True
• False

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

• RNNs represent the recurrent process of Idea->Code->Experiment->Idea->…
• It is applicable when the input/output is a sequence (e.g., a sequence of words).
• It can be trained as a supervised learning problem.
• It is strictly more powerful than a Convolutional Neural Network (CNN).

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

x-axis is the amount of data(x轴是数据量) y-axis (vertical axis) is the
performance of the algorithm.(y轴（垂直轴）是算法的性能)

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.)

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