{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dlopt-deep-learning-optimization-library","title":"DLOPT: Deep Learning Optimization Library","arxiv_id":"1807.03523","date":"2018-07-10","proceeding":null,"authors":["Andrés Camero","Jamal Toutouh","Enrique Alba"],"abstract":"Deep learning hyper-parameter optimization is a tough task. Finding an\nappropriate network configuration is a key to success, however most of the\ntimes this labor is roughly done. In this work we introduce a novel library to\ntackle this problem, the Deep Learning Optimization Library: DLOPT. We briefly\ndescribe its architecture and present a set of use examples. This is an open\nsource project developed under the GNU GPL v3 license and it is freely\navailable at https://github.com/acamero/dlopt","url_abs":"http://arxiv.org/abs/1807.03523v1","url_pdf":"http://arxiv.org/pdf/1807.03523v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dlopt-deep-learning-optimization-library","repo_url":"https://github.com/acamero/dlopt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}