{"id":20291115,"url":"https://github.com/arundo/wids2019-equipment-ad","last_synced_at":"2025-09-05T13:47:36.113Z","repository":{"id":44864832,"uuid":"176903201","full_name":"arundo/wids2019-equipment-ad","owner":"arundo","description":null,"archived":false,"fork":false,"pushed_at":"2022-09-23T22:24:55.000Z","size":5577,"stargazers_count":2,"open_issues_count":3,"forks_count":4,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-04-11T11:52:04.383Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/arundo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-03-21T08:37:06.000Z","updated_at":"2023-09-09T21:40:32.000Z","dependencies_parsed_at":"2023-01-18T16:33:56.640Z","dependency_job_id":null,"html_url":"https://github.com/arundo/wids2019-equipment-ad","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/arundo/wids2019-equipment-ad","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arundo%2Fwids2019-equipment-ad","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arundo%2Fwids2019-equipment-ad/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arundo%2Fwids2019-equipment-ad/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arundo%2Fwids2019-equipment-ad/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/arundo","download_url":"https://codeload.github.com/arundo/wids2019-equipment-ad/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arundo%2Fwids2019-equipment-ad/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273767607,"owners_count":25164461,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-05T02:00:09.113Z","response_time":402,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-14T15:10:33.335Z","updated_at":"2025-09-05T13:47:31.102Z","avatar_url":"https://github.com/arundo.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Build an Anomaly Detection Model using Deep Learning \n**Workshop presented during Women in Data Science Conference 04.04.2019 in Oslo**\n\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://www.eventbrite.co.uk/e/women-in-data-science-oslo-tickets-57507382128\"\u003e\u003cimg width=\"600\" src=\"media/wids_banner.jpeg\" alt=\"WiDS banner\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\nOriginal description is available at [WiDS pages](http://www.wids-oslo.org/). \n\nThe introductory slides are available in the [slides](/slides) folder of this repository. \n\n## Setup \n\nTo reproduce the virtual environment used for the workshop install\n[pipenv](https://pipenv.readthedocs.io/en/latest/) and type:\n\n```bash\npipenv install\n```\n\nThen you should be able to start a jupyter notebook and execute the notebook content.\n\n\n## Data\n\nThe data used in this workshop was extracted from the [Turbofan engine degradation simulation data set](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#turbofan) (dataset ID: \"FD001\"). \n\nReference: Saxena, A., Goebel, K., Simon, D. and Eklund, N., 2008, October. Damage propagation modeling for aircraft engine run-to-failure simulation. In Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1-9). IEEE.\n\n\n## Questions? Suggestions?\n\nIf you have any questions about the presented content or would like to suggest\nways we could improve this tutorial please reach out to us at\n[support@arundo.com](mailto:support@arundo.com).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farundo%2Fwids2019-equipment-ad","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farundo%2Fwids2019-equipment-ad","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farundo%2Fwids2019-equipment-ad/lists"}