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#DataScience

109 posts64 participants0 posts today

The joys of saving money by running analyses with preemptible cloud resources!
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[1] "Out of memory"
NULL

[1] "Preempted"
[1] 1 2 3 16 24 33 39 64 74 103

[1] "No results yet (if they're missing from the above they were preempted before logging)"
4 5 7 14 30 69 107 112

With the introduction of the ShinyProxy Operator for Docker, setting up a production-grade ShinyProxy server has never been easier.

Our new blog post explains why only minimal configuration is needed to handle a large number of concurrent users. Expensive servers or clusters are not required. A single server is sufficient.

Read more: oa.eu/mbhe0b

From the @DSLC :rstats:​chives:

:rstats: Mastering Shiny: Testing youtu.be/7Uz0Mh2gMfk

:rstats: Tidy Modeling with R: youtu.be/ikeiX0W7Xuc

:rstats: Engineering Production Grade Shiny Apps Book Club youtu.be/0PTj3_TUyx4

:rstats: R for Data Science: Databases youtu.be/ylTfwbQq1v0

Visit dslc.video for hours of new videos every week!

Replied in thread

Logistic regression may be used for classification.

In order to preserve the convex nature for the loss function, a log-loss cost function has been designed for logistic regression. This cost function extremes at labels True and False.

The gradient for the loss function of logistic regression comes out to have the same form of terms as the gradient for the Least Squared Error.

More: baeldung.com/cs/gradient-desce