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Neural Networks and Deep Learning Week 1 Quiz Answers Coursera
Question 1: What does the analogy “AI is the new electricity” refer to?
- 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.
- Through the “smart grid”, AI is delivering a new wave of electricity.
- AI is powering personal devices in our homes and offices, similar to electricity.
Question 2: Which of these are reasons for Deep Learning recently taking off? (Check the three options that apply.)
- We have access to a lot more data.
- Neural Networks are a brand new field.
- We have access to a lot more computational power.
- Deep learning has resulted in significant improvements in important applications such as online advertising, speech recognition, and image recognition.
Question 3: Recall this diagram of iterating over different ML ideas. Which of the statements below are true? (Check all that apply.) IDEA->CODE->EXPERIMENT
- 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).
Question 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?
Question 5: Which one of these plots represents a ReLU activation function?
- Figure 1:
- Figure 2:
- Figure 3:
- Figure 4:
Question 6: Images for cat recognition is an example of “structured” data, because it is represented as a structured array in a computer. True/False?
Question 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?
Question 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->….
Question 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.
- x-axis is the performance of the algorithm
- y-axis (vertical axis) is the amount of data.
- x-axis is the input to the algorithm
- y-axis is outputs.
- x-axis is the amount of data
- y-axis is the size of the model you train.
Question 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 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.
- Increasing the training set size generally does not hurt an algorithm’s performance, and it may help significantly.
- Decreasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly.