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# Automated Deep Learning (AutoDL)
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[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE.md)
Automated Deep Learning (AutoDL-Projects) is an open source, lightweight, but useful project for researchers.
This project implemented several neural architecture search (NAS) and hyper-parameter optimization (HPO) algorithms.
## **Who should consider using AutoDL-Projects**
**Who should consider using AutoDL-Projects**
- Beginners who want to **try different AutoDL algorithms**
- Engineers who want to **try AutoDL** to investigate whether AutoDL works on your projects
- Researchers who want to **easily** implement and experiement **new** AutoDL algorithms.
## **Why should we use AutoDL-Projects**
**Why should we use AutoDL-Projects**
- Simple library dependencies
- All algorithms are in the same codebase
- Active maintenance
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<tr> <!-- (2-nd row) -->
<td align="center" valign="middle"> DARTS </td>
<td align="center" valign="middle"> DARTS: Differentiable Architecture Search </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/master/docs/NAS-Bench-201.md">CVPR-2019-GDAS.md</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/master/docs/NAS-Bench-201.md">NAS-Bench-201.md</a> </td>
</tr>
<tr> <!-- (3-nd row) -->
<td align="center" valign="middle"> GDAS </td>