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Title here is wrong. Title in article and headings in article are right: ONLINE gradient descent It's specifically not stochastic. From the article: Online
by Terretta 4y ago
Title here is wrong. Title in article and headings in article are right: ONLINE gradient descent
It's specifically not stochastic. From the article:
Online gradient descent
Finally, we have enough experience to implement online gradient descent. To keep things simple, we will use a very vanilla version:
- Constant learning rate, as opposed to a schedule.
- Single epoch, we only do one pass on the data.
- Not stochastic: the rows are not shuffled. ⇠ ⇠ ⇠ ⇠
- Squared loss, which is the standard loss for regression.
- No gradient clipping.
- No weight regularisation.
- No intercept term.
- Lemaxoxo 4y agoHehe I was wondering if someone would catch that. Rest assured, I know the difference between online and stochastic gradient descent. I admit I used stochastic on Hacker News because I thought it would generate more engagement.
- airstrike 4y agoThen just call it Non-stochastic Gradient Descent? You can't editorialize titles per HN guidelines https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
- westurner 4y agoWhat are some adversarial cases for gradient descent, and/or what sort of e.g. DVC.org or W3C PROV provenance information should be tracked for a production ML workflow? Gradient descent: https://en.wikipedia.org/wiki/Gradient_descent https://en.wikipedia.org/wiki/Gradient_descent Stochastic gradient descent: https://en.wikipedia.org/wiki/Stochastic_gradient_descent https://en.wikipedia.org/wiki/Stochastic_gradient_descent Online machine learning: https://en.wikipedia.org/wiki/Online_machine_learning https://en.wikipedia.org/wiki/Online_machine_learning adversarial gradient descent site:github.com inurl:awesome : https://www.google.com/search?q=awesome+adversarial+gradient+descent+site%3Agithub.com https://www.google.com/search?q=awesome+adversarial+gradient... https://github.com/EthicalML/awesome-production-machine-learning#adversarial-robustness https://github.com/EthicalML/awesome-production-machine-lear... Robust machine learning: https://en.wikipedia.org/wiki/Robustness_(computer_science)#Robust_machine_learning https://en.wikipedia.org/wiki/Robustness_(computer_science)#... Robust gradient descent
- craigacp 4y agoWe built model & data provenance into our open source ML library, though it's admittedly not the W3C PROV standard. There were a few gaps in it until we built an automated reproducibility system on top of it, but now it's pretty solid for all the algorithms we implement. Unfortunately some of the things we wrap (notably TensorFlow) aren't reproducible enough due to some unfixed bugs. There's an overview of the provenance system in this reprise of the JavaOne talk I gave here https://www.youtube.com/watch?v=GXOMjq2OS_c https://www.youtube.com/watch?v=GXOMjq2OS_c. The library is on GitHub - https://github.com/oracle/tribuo https://github.com/oracle/tribuo.
- deleted 4y ago[deleted]