Domain > courses.d2l.ai

More information on this domain is in AlienVault OTX

DNS Resolutions

Date	IP Address
2020-12-01	54.230.74.16 (ClassC)
2020-12-01	54.230.74.36 (ClassC)
2022-06-20	13.249.85.13 (ClassC)
2024-08-08	204.246.191.62 (ClassC)
2024-08-29	18.161.6.125 (ClassC)
2025-04-05	204.246.191.119 (ClassC)
2025-08-14	18.161.6.18 (ClassC)
2025-10-03	18.161.6.55 (ClassC)

HTTP/1.1 200 OKContent-Type: text/htmlContent-Length: 6988Connection: keep-aliveLast-Modified: Tue, 17 Aug 2021 21:24:36 GMTx-amz-version-id: nullServer: AmazonS3Date: Thu, 08 Aug 2024 10:53:14 GMTETa

!DOCTYPE html> html> head> meta charsetutf-8> meta http-equivX-UA-Compatible contentIEedge> meta nameviewport contentwidthdevice-width, initial-scale1.0, minimum-scale1.0, maximum-scale1.0> title>D2L Courses/title> link relcanonical hrefhttp://localhost:4008/ /> meta namedescription content> meta namerobots contentindex,follow> meta itempropname contentD2L Courses> meta nametheme-color content#f2f2f2> link href/assets/css/materialize.min.css relstylesheet> link href/assets/css/main.css relstylesheet> link relshortcut icon href/favicon.ico typeimage/x-icon> link hrefhttps://fonts.googleapis.com/css?familyRoboto relstylesheet> style>body{background:#f2f2f2;}/style> /head> body> div classcontainer row> h1 classcv-title>span classblue white-text>D2L Courses/span>/h1> div classitem> i classvertical-line>/i> h2 classitem-date>09/2021 - 12/2021/h2> div classcard-panel> div classtop-right> img width160px, srcassets/images/stanford-logo.png>/img> /div> h2 classcard-title> a hrefhttps://c.d2l.ai/stanford-cs329p/>Practical Machine Learning/a> /h2> h3 classvenue> CS329P, Stanford /h3> h3 classauthor> a hrefhttps://www.linkedin.com/in/smola/>Alex Smola,/a> a hrefhttps://www.linkedin.com/in/qingqing-huang-18a329138/>Qingqing Huang,/a> a hrefhttps://www.linkedin.com/in/mulicmu/>Mu Li/a> /h3> p> p>Applying Machine Learning (ML) to solve real problems accurately and robustly requires more than just training the latest ML model. First, you will learn practical techniques to deal with data. This matters since real data is often not independently and identically distributed. It includes detecting covariate, concept, and label shifts, and modeling dependent random variables such as the ones in time series and graphs. Next, you will learn how to efficiently train ML models, such as tuning hyper-parameters, model combination, and transfer learning. Last, you will learn about fairness and model explainability, and how to efficiently deploy models. This is a full semester class that teaches both statistics, algorithms and code implementa

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Domain > courses.d2l.ai

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