# Sources {% embed url="" %} Sources - Data Management {% endembed %} ## 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/data-management/sources.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.