{"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/nuts-flowml-data-pre-processing-for-deep","title":"nuts-flow/ml: data pre-processing for deep learning","arxiv_id":"1708.06046","date":"2017-08-21","proceeding":null,"authors":["S. Maetschke","R. Tennakoon","C. Vecchiola","R. Garnavi"],"abstract":"Data preprocessing is a fundamental part of any machine learning application\nand frequently the most time-consuming aspect when developing a machine\nlearning solution. Preprocessing for deep learning is characterized by\npipelines that lazily load data and perform data transformation, augmentation,\nbatching and logging. Many of these functions are common across applications\nbut require different arrangements for training, testing or inference. Here we\nintroduce a novel software framework named nuts-flow/ml that encapsulates\ncommon preprocessing operations as components, which can be flexibly arranged\nto rapidly construct efficient preprocessing pipelines for deep learning.","url_abs":"http://arxiv.org/abs/1708.06046v2","url_pdf":"http://arxiv.org/pdf/1708.06046v2.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":"nuts-flowml-data-pre-processing-for-deep","repo_url":"https://github.com/maet3608/nuts-ml","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"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}