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  • May 20 05:04

    ocramz on gh-pages

    Add `sampling` (compare)

  • May 19 09:03

    ocramz on gh-pages

    Add kdt, Supervised Learning se… (compare)

  • Apr 14 01:32
    tonyday567 removed as member
  • Jan 30 07:37

    ocramz on gh-pages

    Add arrayfire (compare)

  • Jan 02 12:51

    ocramz on gh-pages

    add inliterate (compare)

  • Jan 02 12:43

    ocramz on gh-pages

    update hvega entry (compare)

  • Jul 01 2019 09:43
    dmvianna added as member
  • Jun 15 2019 04:55

    ocramz on gh-pages

    Add pcg-random (compare)

  • Jun 14 2019 16:08
    ocramz labeled #42
  • Jun 14 2019 16:08
    ocramz labeled #42
  • Jun 14 2019 16:08
    ocramz labeled #42
  • Jun 14 2019 16:08
    ocramz labeled #42
  • Jun 14 2019 16:08
    ocramz labeled #42
  • Jun 14 2019 16:08
    ocramz labeled #42
  • Jun 14 2019 16:08
    ocramz opened #42
  • Jun 14 2019 16:08
    ocramz opened #42
  • Jun 06 2019 18:21

    ocramz on gh-pages

    Fix graphite link Merge pull request #41 from alx… (compare)

  • Jun 06 2019 18:21
    ocramz closed #41
  • Jun 06 2019 18:21
    ocramz closed #41
  • Jun 06 2019 17:32
    alx741 opened #41
Alejandro Alcalde
@elbaulp
@ocramz the section “Data Structures” in here for example. http://www.datahaskell.org/docs/community/current-environment.html#data-structures
Marco Z
@ocramz
@elbaulp the current-environment page is a directory of libraries generally built by others
Alejandro Alcalde
@elbaulp
@ocramz I see... I will focus by now in learning Haskell and later on will try to see if I can contribute.
Ada922
@Ada922

Tomorrow Algorand is hosting a live webinar on implementing the world’s first CBDC. I have submitted a few questions and will be attending. This is a great way to spend your quarantine :)

Register and submit questions here: https://register.gotowebinar.com/register/5273854108710515471

Karl-Oskar Rikås
@funrep
anyone familiar with random-fu who knows why gamma does not define a pdf?
3 replies
Alexey Kuleshevich
@lehins
@funrep Not sure, but I know a person who might know the answer to that question. @idontgetoutmuch any ideas? ^
Karl-Oskar Rikås
@funrep
not a big issue because it's defined in the statistics package, just wondered in case there is some mathematical reason im unaware of :)
anyone know how I can make a set a tensor.require_grad = True in hasktorch?
Karl-Oskar Rikås
@funrep
https://gist.github.com/funrep/ced4c79fd97cbc945e8a5a83c4b30dc8 not really sure I understand how to use the grad API, i want to perform AD using hasktorch to compute the gradient in stochastic gradient descent
would be cool to use the optimizer api directly, but starting out with just the grad as I didn't find any bindings to zero_grad
Austin Huang
@austinvhuang
Screenshot 2020-05-31 at 12.28.18 PM.png
@funrep Just saw this. I'm in the middle of writing up a step-by-step tutorial in fact... here's a rough draft of the ad intro
Screenshot 2020-05-31 at 12.28.36 PM.png
super early version... this is for untyped tensors, working on sections for typed tensors. will have more on optimization but hopefully this at least gets the basic ideas on AD and how requires_grad looks like from the Haskell side of things
Karl-Oskar Rikås
@funrep
@austinvhuang hey thanks a lot, where will the tutorial be published?
Marco Z
@ocramz
@funrep you could use the 'gamma' package and implement the Gamma pdf from the Gamma function
Austin Huang
@austinvhuang
@funrep will eventually get the writeup on arxiv, but I think I'll also make a markdown version to include in the repo. there's a section that's basically a port of pytorch's 60 min blitz.
masterdezign
@masterdezign
Hi @austinvhuang, just wonder if the tutorial is already available
Adam Conner-Sax
@adamConnerSax
Hi! I put a new version of “knit-haskell” (https://hackage.haskell.org/package/knit-haskell-0.8.0.0) on hackage today. Adds a few features. The most useful one, for my workflow, is a caching system. Allows caching of anything serializable to an in-memory map and also disk. Has some basic but useful support for tracking when cached items need to be rebuilt. See the readme and examples for more info! All feedback/ideas most welcome.
NotBru
@NotBru
Hi there! I'm a physics graduate trying to think of a way to delve into AI and data science, but I'm quite lost as to where to start
Tony Day
@tonyday567
@NotBru hi!
Tony Day
@tonyday567
I'm working on a really interesting, haskelly side-project called ephemeral.
https://github.com/tonyday567/ephemeral/blob/master/src/Ephemeral/Learning.hs#L15
I'm connecting up a GA library, moo, with an auto-differentiation library, backprop. Trying to nail down what it means to Learn.
That quote is like page 1 of the cousera course. And I think it's a great start. I'm trying to think in types.
NotBru
@NotBru
@tonyday567 thanks a lot for the answer! will check it out. I'm still just delving into Haskell, too, so it's gonna take me some time
Tony Day
@tonyday567
We have time!
masterdezign
@masterdezign
@NotBru Hi! I have a small blog on deep learning in Haskell http://penkovsky.com/neural-networks/
Austin Huang
@austinvhuang
@masterdezign - it's not online yet but I can share a wip draft with you.
Tony Day
@tonyday567
I'm finally getting a productive covid workflow and have a few announcements on library releases.
Tony Day
@tonyday567
https://hackage.haskell.org/package/numhask is an alternative prelude, that plugs in an industrial strength numeric class tower, if needed. It wraps protolude from the wonderful @sdiehl, is inclined towards Text as the string primitive, and has less partial functions than is good for you.
Everyone should use an alternative prelude, and this is mine.
Tony Day
@tonyday567
Upstream, I'm pleased to announce that https://hackage.haskell.org/package/chart-svg has become quite stable, and better documented. A chart server is included that is quite addictive to use in prettying up a chart. SVG provides an exact medium in which to render, and is a versatile but stable API. Give it a go!
Alexey Kuleshevich
@lehins
@tonyday567 chart-svg looks amazing!!!!!
NotBru
@NotBru

@NotBru Hi! I have a small blog on deep learning in Haskell http://penkovsky.com/neural-networks/

Thanks!!

Tony Day
@tonyday567
Thanks @lehins
Tony Day
@tonyday567
@lehins hook it up to massive and you could render a movie I reckon.
Alexey Kuleshevich
@lehins
:smile:
Alexey Kuleshevich
@lehins
Later on I will give chart-svg a shot for plotting image histograms in HIP. I don't want to use Chart library for this because it depends on lens, which brings the whole kmettoverse with it. So I am really stoked to get a lightweight solution for plots in pure Haskell.
Tony Day
@tonyday567
lens is a dependency, but I use it heavily in connection with OverloadedLabels, so there is zero lens usage requirement. If I ever had the inclination, I could store a Chart it in a dhall file.
In chart-svg, histograms are equally-spaced rectangles (RectA's). There's support in numhask-space for a histogram primitive, but Chart.Bar probably has everything you need for a nice-looking, lightweight chart.
Alexey Kuleshevich
@lehins

lens is a dependency

Dammit, didn't look at that. In that case I'll have to weigh if it is worth to switch from Charts. I prefer to keep my packages very light on dependencies and lens with all its baggage is not acceptable for me. I might release a separate package that just does the image histogram plotting (that my original plan for Charts at least).

Tony Day
@tonyday567
Yeah, it's easy. Just shapes!
Marco Z
@ocramz
what's up gang
Stefan Dresselhaus
@Drezil
waiting for haskell.love ;)
Marco Z
@ocramz
is it today?
Alexey Kuleshevich
@lehins
nah, over the next weekend
Austin Huang
@austinvhuang
@NickSeagull Do you see tintin being developed further in the future?
Nick Tchayka
@NickSeagull
@austinvhuang We're not actively developing this project. We're using it in a few other projects, like the AWS lambda runtime, so we'll be occasionally updating it if we find a non met requirement there, but we'd be glad to review and merge community contributions.
Randall Britten
@vertexcite
Bumped into this at ICFP today: https://dl.acm.org/doi/10.1145/3406088.3409019
See also https://github.com/bolt12/laop (I think).
Callan McGill
@Boarders
Does anyone familiar with massiv know how parallelization works with a function like this: https://hackage.haskell.org/package/massiv-0.5.4.0/docs/Data-Massiv-Array.html#v:mapIO_
Alexey Kuleshevich
@lehins
@Boarders it will work in exactly same way as when the delayed array is being loaded: split the array in as many chunks as there are cores and map an IO action over each of them in a row major fashion in parallel. I talk a bit about in here: https://youtu.be/AAx2a0bUsxA?t=2215