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Case Study: Designing a cat detection dev/test set before mobile application launch.
Case context: You are building a mobile application to detect cats in user photos, but the app has not yet launched. Because you have no users, you do not have any actual user data to construct your dev and test sets.
Question: Based on the concept of approximating future dev/test data, how should you proceed to build your dev/test sets, and what is a specific action you can take to collect data?
Sample answer: You should proceed by trying to approximate the future user data distribution. To do this, you can ask your friends to take mobile phone pictures of cats and send them to you, which serves as a proxy to build the initial dev/test sets.
Key points:
- Dev/test sets should be constructed pre-launch by approximating future data.
- Proxy data collection is required since actual user data is unavailable.
- Asking friends to take mobile-phone pictures of cats is a valid approximation method.
Rubric: The response must identify the need to approximate the future user data distribution and propose the specific action of asking friends to take mobile phone pictures of cats.
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Before a mobile app launches, how can you approximate future dev/test data when no user data is available?
Before a mobile app launches, it is impossible to approximate the future data distribution for dev/test sets.
Before launching a mobile app, you can approximate future user data by asking friends to take _____ pictures of cats and send them to you.
When a mobile app has not yet launched and no user data is available, what is the recommended approach for building dev/test sets?
Before a mobile app launches, it is impossible to construct any dev/test set because real user data does not yet exist.
When no user data exists before app launch, Andrew Ng recommends trying to _____ the future data distribution for dev/test sets.
Match each pre-launch data concept to its correct description.
Order the steps a team should follow to approximate pre-launch dev/test data for a mobile app.
Which specific example does Andrew Ng give in Machine Learning Yearning for approximating pre-launch mobile app data for a cat-detection use case?
Data collected from friends before a mobile app's launch perfectly represents the distribution of future user data.
Andrew Ng suggests asking your _____ to take mobile-phone pictures of cats as one way to approximate pre-launch user data.
Match each pre-launch data approximation term to its correct explanation.
Order the reasoning steps for deciding how to handle the absence of user data when constructing dev/test sets before a mobile app launches.
Explain the rationale and challenges of approximating future dev/test data before a mobile application launch.
Case Study: Designing a cat detection dev/test set before mobile application launch.
What is the purpose of asking friends to take photos when constructing a pre-launch dev/test set?