ocramz on gh-pages
Add `sampling` (compare)
ocramz on gh-pages
Add kdt, Supervised Learning se… (compare)
ocramz on gh-pages
Add arrayfire (compare)
ocramz on gh-pages
add inliterate (compare)
ocramz on gh-pages
update hvega entry (compare)
ocramz on gh-pages
Add pcg-random (compare)
ocramz on gh-pages
Fix graphite link Merge pull request #41 from alx… (compare)
The biggest challenge is the ecosystem needs to be implemented, differentiated, and matured. Python as a language isn't inherently suited for data science, but the fact that pandas, sklearn, pyspark, pytorch, tensorflow, seaborn, plotly, etc. exist and have had rough edges smoothed out by years of use cases makes it the defacto standard.
yeah exactly
CREATE
tables from a HashMap Text TypeRep
, supposing each TypeRep
can correspond to a Sql numeric/string type (not looking for an ORM)@rohakapo sorry, I'll turn on the notifications.
I'm very beginner at haskell and also I'm very beginner at DataScience.
But I love haskell and this language give me more satisfaction than anything in IT.
I'm looking to make a carer in data science. I'm from python backend word.
Any advice?
hvega
docs - in particular for the "how do I actually display the damn thing" - are appreciated at https://github.com/DougBurke/hvega . I'd particularly like to improve the IHaskell story, but that (I believe) requires IHaskell work which I haven't had the energy to dig into.
spec = A.encodeToLazyText (fromVL vl)
should do - then save it to a file you can then use https://github.com/DougBurke/vega-view (or the vega desktop application) to view it (it runs a web server that will let you view the .vg.json files in the working directory as well as allowing you to drag-n-drop files onto the browser).
+1 hvega from me too. vega-lite itself has been such a boon to other languages, thanks for your work on hvega @DougBurke.
@YPares did you ever find a sql solution? i'm using sqlite-simple a lot... i can live with sqlite-simple but i think i have to bite the bullet on some minimal string builder on my current project. it'd be nice to have more lightweight string-builder middle-ground options between hard-coding queries as strings and full-on orm commitment.
@frasermince can ping us in the hasktorch slack if you'd like to get a hasktorch kaggle effort going. will say kaggle is probably a space where haskell has few comparative advantages (there's no penalty to scripts being hacky/throwaway as long as they score the test data, this is not true of "real-world" ml).