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# Auto Deep Learning (AutoDL) # Automated Deep Learning (AutoDL)
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[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE.md) [![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE.md)
Auto Deep Learning by DXY (AutoDL-Projects) is an open source, lightweight, but useful project for researchers. Automated Deep Learning (AutoDL-Projects) is an open source, lightweight, but useful project for researchers.
In this project, Xuanyi Dong implemented several neural architecture search (NAS) and hyper-parameter optimization (HPO) algorithms. This project implemented several neural architecture search (NAS) and hyper-parameter optimization (HPO) algorithms.
He hopes to build it as an easy-to-use AutoDL toolkit in future.
## **Who should consider using AutoDL-Projects** ## **Who should consider using AutoDL-Projects**
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<td align="center" valign="middle"> One-Shot Neural Architecture Search via Self-Evaluated Template Network </td> <td align="center" valign="middle"> One-Shot Neural Architecture Search via Self-Evaluated Template Network </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/master/docs/ICCV-2019-SETN.py">ICCV-2019-SETN.py</a> </td> <td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/master/docs/ICCV-2019-SETN.md">ICCV-2019-SETN.md</a> </td>
</tr> </tr>
<tr> <!-- (4-th row) --> <tr> <!-- (4-th row) -->
<td align="center" valign="middle"> NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search </td> <td align="center" valign="middle"> NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search </td>

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docs/ICCV-2019-SETN.py → docs/ICCV-2019-SETN.md
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