# Autonomous driving (case study)

## Autonomous driving (case study) >> Structuring Machine Learning Projects

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#### Autonomous driving (case study)

TOTAL POINTS 15

You are employed by a startup building self-driving cars. You are in charge of detecting road signs (stop sign, pedestrian crossing sign, construction ahead sign) and traffic signals (red and green lights) in images. The goal is to recognize which of these objects appear in each image. As an example, the above image contains a pedestrian crossing sign and red traffic lights

Your 100,000 labeled images are taken using the front-facing camera of your car. This is also the distribution of data you care most about doing well on. You think you might be able to get a much larger dataset off the internet, that could be helpful for training even if the distribution of internet data is not the same.

You are just getting started on this project. What is the first thing you do? Assume each of the steps below would take about an equal amount of time (a few days).

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For the output layer, a softmax activation would be a good choice for the output layer because this is a multi-task learning problem. True/False?

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• 100,000 labeled images taken using the front-facing camera of your car.
• Each image’s labels precisely indicate the presence of any specific road signs and traffic signals or combinations of them. For example, means the image contains a stop sign and a red traffic light.

Because this is a multi-task learning problem, you need to have all your y^{(i)} vectors fully labeled. If one example is equal to

then the learning algorithm will not be able to use that example. True/False?
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 Dataset: Contains: Error of the algorithm: Training 940,000 images randomly picked from (900,000 internet images + 60,000 car’s front-facing camera images) 8.8% Training-Dev 20,000 images randomly picked from (900,000 internet images + 60,000 car’s front-facing camera images) 9.1% Dev 20,000 images from your car’s front-facing camera 14.3% Test 20,000 images from the car’s front-facing camera 14.8%

You also know that human-level error on the road sign and traffic signals classification task is around 0.5%. Which of the following are True? (Check all that apply).

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 Overall dev set error 15.3% Errors due to incorrectly labeled data 4.1% Errors due to foggy pictures 8.0% Errors due to rain drops stuck on your car’s front-facing camera 2.2% Errors due to other causes 1.0%

In this table, 4.1%, 8.0%, etc. are a fraction of the total dev set (not just examples your algorithm mislabeled). For example, about 8.0/15.3 = 52% of your errors are due to foggy pictures.

The results from this analysis implies that the team’s highest priority should be to bring more foggy pictures into the training set so as to address the 8.0% of errors in that category. True/False?

Additional Note: there are subtle concepts to consider with this question, and you may find arguments for why some answers are also correct or incorrect. We recommend that you spend time reading the feedback for this quiz, to understand what issues that you will want to consider when you are building your own machine learning project.

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Which of the following statements do you agree with?

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What do you tell your colleague?

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• (A) Input an image (x) to a neural network and have it directly learn a mapping to make a prediction as to whether there’s a red light and/or green light (y).

A teammate proposes a different, two-step approach:

• (B) In this two-step approach, you would first (i) detect the traffic light in the image (if any), then (ii) determine the color of the illuminated lamp in the traffic light.

Between these two, Approach B is more of an end-to-end approach because it has distinct steps for the input end and the output end. True/False?

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