Evaluating the source of data growth for a mobile health application.
Case context: A startup is launching a mobile health tracking application. They notice a massive increase in the volume of user interactions and telemetry data collected daily compared to older desktop-only health applications, and they want to leverage this to improve their models.
Question: Explain, using the concept of digital-device activity and data availability described by Andrew Ng, why this mobile application generates such a vast amount of data and how this feeds back into their deep learning models.
Sample answer: The mobile app generates vast data because users spend significant time on mobile digital devices. Their digital activities on these devices create huge amounts of data. According to Andrew Ng, this high data availability can be fed directly to the startup's learning algorithms, driving progress in their deep learning models.
Key points:
- User interactions on mobile digital devices generate huge amounts of data.
- This increases data availability for the startup's models.
- The data can be fed into learning algorithms to improve them.
- Increased data availability drives deep learning progress.
Rubric: Students should specify that user activity on mobile digital devices generates huge volumes of data, and that this data availability is fed to learning algorithms to drive deep learning progress.
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Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
According to Andrew Ng, what is the primary reason data availability has increased to benefit deep learning?
Data availability is listed as one of the two biggest drivers of recent deep learning progress in Machine Learning Yearning.
People spending more time on _____ devices—such as laptops and mobile devices—generates large amounts of data for learning algorithms.
Match each component of the data-availability argument to its role in driving deep learning progress.
Order the steps in the causal chain from digital-device use to deep learning progress as described in Machine Learning Yearning.
Which statement best captures Andrew Ng's view of the link between digital-device usage and deep learning progress?
According to Machine Learning Yearning, data availability is the sole driver of recent deep learning progress.
According to Machine Learning Yearning, digital activities generate _____ amounts of data that can be fed to learning algorithms.
Match each part of Andrew Ng's p. 9 argument to its logical function in the data-availability explanation.
Arrange the steps in the order Andrew Ng uses them to build his argument for data availability as a driver of deep learning progress on p. 9.
Explain the relationship between digital device usage and deep learning progress.
Evaluating the source of data growth for a mobile health application.
How does modern digital device usage directly impact data availability for learning algorithms?