> For the complete documentation index, see [llms.txt](https://fall2019.fullstackdeeplearning.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://fall2019.fullstackdeeplearning.com/course-content/data-management/sources.md).

# Sources

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Sources - Data Management
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## Summary

* Most deep learning applications require lots of labeled data. There are publicly available datasets that can serve as a starting point, but there is no competitive advantage of doing so.
* Most companies usually spend a lot of money and time to label their own data.
* **Data flywheel** means harnessing the power of users rapidly improve the whole machine learning system.
* **Semi-supervised learning** is a relatively recent learning technique where the training data is autonomously (or automatically) labeled.
* **Data augmentation** is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data.
* **Synthetic data** is data that’s generated programmatically, an underrated idea that is almost always worth starting with.