@krasul thanks for the great blog post on probabilistic time series. How do I generate out of sample predictions using the vanilla transformer for time series? I was able to run through the entire tutorial, but not sure how to make predictions beyond my validation set.
@krasul Thanks. I am wondering if I can reuse the test loader from the post for this, or if a modification is needed. I’ll check.
I can also post on hugging face. Not sure if you’re on discord. Thanks.
@hrbrmstr If I've installed this font on my system (macOS) and run import_*, is there a reason why R cannot locate the font?
In https://t.co/wempnWT4OE(C_stringMetric, as.graphicsAnnot(x$label)) :
no font could be found for family "IBMPlexSans"
@ChelseaParlett You don’t realize the state you’re in when you’re in it. So I think it’s normal to feel dumb doing difficult stuff, but then when you reflect later in life you’ll feel like “damn I was smart figuring that out.”
I read a quick brief on software versioning. It said versions of software go something like this:
12.6.33
where 12 stands for major releases. This 12 release has had 6 minor releases and 33 bug fixes.
Are there any opinions if version ML models should follow something similar?
purrr::pluck() lets you deftly index into data structures 🦎 Fun to use, bonus you can say "purrrpluck purrrpluck purrrpluck" the whole time 👍
https://t.co/Ok0mWch9bW
I get asked a lot about what does it take to land a job at DeepMind or any other world-class AI industry lab.
For those of you that are unaware of it I wrote a detailed blog on that topic and shared my personal journey here: https://t.co/f5PwyYrHI2
If I could summarize..
1/ 🧶
I am forgetting the name of the plots where you have numerous density plots lined up by a variable, say a date…does anyone remember the name? #tidyverse#ggplot2
I've updated my Data Wrangling in the Tidyverse course material and am uploading a 17-part video series. This assumes little previous R knowledge (although some). Covers tidying, manipulating variables, cleaning factors, dates, and strings. Enjoy! https://t.co/rVZsuGd2IU
Generalized Additive Models (GAMs) are fully interpretable ML models, unlike DNNs, but it's hard to make them efficient. We're sharing research on scaling GAMs to real world tasks w/o sacrificing accuracy or interpretability. https://t.co/9a9zaP4kdN https://t.co/1IxxVceeu2 [1/4]