# Synthetic Data [Data is the new oil](https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data). The aphorism is a bit cliche but it is true that the tech giants have benefited disproportionately from AI which is due in no small part to the amount of data they collect. ![Source: The Economist](/files/-LwQ_Nnp-Qmz70Q70KwG) Companies that are not Google, Facebook, Amazon et al. often do not have enough data to train models accurately -- especially in the case of training deep neural networks that require more data than classical machine learning algorithms. Creation of fake data, called synthetic data, is one way of overcoming the lack of data. This burgeoning technique can be used to generate all kinds of datasets including images, audio files, and more. This is often proceeded by [transfer learning](/wiki/transfer-learning.md) during which models are deployed to similar problems where there may be substantial developmental overlap, thus reducing time and effort when compared to starting from scratch. --- # 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://machine-learning.paperspace.com/wiki/synthetic-data.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.