Deep Learning Tuning Playbook. December, 2023. original-date: 2023-01-18T23:32:32Z
Paper abstract bibtex A playbook for systematically maximizing the performance of deep learning models.
@misc{noauthor_deep_2023,
title = {Deep {Learning} {Tuning} {Playbook}},
url = {https://github.com/google-research/tuning_playbook},
abstract = {A playbook for systematically maximizing the performance of deep learning models.},
urldate = {2023-12-12},
publisher = {Google Research},
month = dec,
year = {2023},
note = {original-date: 2023-01-18T23:32:32Z},
keywords = {/unread},
}
Downloads: 0
{"_id":"2GX6GARgokvBRFJsk","bibbaseid":"anonymous-deeplearningtuningplaybook-2023","bibdata":{"bibtype":"misc","type":"misc","title":"Deep Learning Tuning Playbook","url":"https://github.com/google-research/tuning_playbook","abstract":"A playbook for systematically maximizing the performance of deep learning models.","urldate":"2023-12-12","publisher":"Google Research","month":"December","year":"2023","note":"original-date: 2023-01-18T23:32:32Z","keywords":"/unread","bibtex":"@misc{noauthor_deep_2023,\n\ttitle = {Deep {Learning} {Tuning} {Playbook}},\n\turl = {https://github.com/google-research/tuning_playbook},\n\tabstract = {A playbook for systematically maximizing the performance of deep learning models.},\n\turldate = {2023-12-12},\n\tpublisher = {Google Research},\n\tmonth = dec,\n\tyear = {2023},\n\tnote = {original-date: 2023-01-18T23:32:32Z},\n\tkeywords = {/unread},\n}\n\n\n\n\n\n\n\n\n\n\n\n","key":"noauthor_deep_2023","id":"noauthor_deep_2023","bibbaseid":"anonymous-deeplearningtuningplaybook-2023","role":"","urls":{"Paper":"https://github.com/google-research/tuning_playbook"},"keyword":["/unread"],"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"misc","biburl":"https://bibbase.org/zotero/zzhenry2012","dataSources":["nZHrFJKyxKKDaWYM8"],"keywords":["/unread"],"search_terms":["deep","learning","tuning","playbook"],"title":"Deep Learning Tuning Playbook","year":2023}