AlexTCH
313 subscribers
77 photos
4 videos
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906 links
Что-то про программирование, что-то про Computer Science и Data Science, и немного кофе. Ну и всякая чушь вместо Твиттера. :)
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Do you like EDM? No, not that EDM, this EDM! 😂
Образцово-показательный проект на "современном JavaScript": https://github.com/lukeed/preact-progress
Один (sic!) файл исходников и два (sic!!!) файла конфигурации системы сборки. Это они ещё package-lock.json не закоммитили и нет отдельного конфига для Babel.
Если вы думаете, что это единственный такой проект — посмотрите по Гитхабу повнимательнее.
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Кто катит в кубер по утрам
Тот поступает мудро!
Известно всем, парам-парам,
На то оно и утро!

Скучна вечерняя пора,
Девопсеры зевают.
Но если катим мы с утра
Такого не бывает!
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"time-series of image data" — а я всегда думал, что это называется видео... 🤔
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https://www.youtube.com/channel/UCzipicZiZ1843jAqmSGgYwg

Video recordings of talks from "50 years of Smalltalk". It has a talk on theorem proving... 🤔
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https://news.ycombinator.com/item?id=11941656

Under the linked message advertising Luna language (now Enso) there's a thread between Alan Kay and Rich Hickey concerning "Data vs. Objects".

The most immediately striking thing is that Rich is much more thoughtful and deep than he seems! 😅

Adding to the discussion, I guess the fact (a datum?! 🤔) that we can decipher ancient languages for which we have no interpreters around for millennia to large extend might hint that (structured) data is indeed kinda more fundamental than objects...
http://aitp-conference.org/2022/

Apparently, there's an annual conference on all things AI and Mechanised Theorem Proving (automatic, interactive and their integration). Topics cover everything from mining archives of formal proofs to proving correctness of ML algorithms to AGI applications.

This year's speaker lineup is insane from Ben Goertzel and Stephen Wolfram to Kevin Buzzard and Talia Ringer.
For those who still doesn't follow closely Lawrence Paulson's blog (shame on you!) he reviews Turing Machines, some other equivalent formalisms and the history of their development: https://lawrencecpaulson.github.io/2022/07/06/Turing_Machines.html
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https://blog.brownplt.org/2022/06/28/static-python.html

> This work focuses on the Static Python language built by the Instagram team at Meta.
🧐

> In particular, we find that the design holds up the intent of soundness well, but the act of modeling it uncovered several bugs (including one that produced a segmentation fault), all of which have now been fixed.
🤔

> As an aside, this paper is a collaboration that was born entirely thanks to Twitter and most probably would never have occurred withtout it.
😁
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https://arrdem.com/2022/07/04/superficial_simplicity/

A reflection on "Simple Made Easy", "Growing a Language", macros and complexity management. Points out (once again) the tension between user-extensibility and tool-analysability.
https://nickchk.com/causalgraphs.html
Causal inference! With animations! 😄

The post explains and illustrates basic notions and methods of causal inference with examples from econometrics. And animated plots, yep.

#causalinference #statistics
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Если уж считаете меня клоуном, то хотя бы грустным клоуном!
https://medium.com/@Reisen_0/a-review-of-the-case-against-education-bacc120cb8cd

A very interesting and thoughtful piece on Human Capital vs. Signalling in Education. Pretty fascinating topic, many academic references, great read.
https://nickch-k.github.io/DataVizChecklist/

A concise "obvious" but totally useful #data #visualization #checklist

Actually I'd say it does not emphasize enough the need for a meaningful story before you try to visualize it. And if you're preparing visualization for an academic publication does not emphasize enough the importance of large enough readable titles and captions. 😄
https://lost-stats.github.io/
"LOST is a Rosetta Stone for statistical software"

Or "Rosetta Code". Useful reference either way.

#statistics #datascience
https://econml.azurewebsites.net/spec/spec.html
The EconML Python SDK, developed by the ALICE team at MSR New England, incorporates individual machine learning steps into interpretable causal models.

Pretty cool. Docs feature introduction into the topic and the methods.

#datascience #causalinference #machinelearning
#machinelearning for 4-graders (~10 years old)
https://orangedatamining.com/blog/2022/2022-06-01-blog-minions-new/

Most important points IMO:
- Single simple task: classification with decision trees
- Guide pupils to invent the method themselves on simplified visual and familiar synthetic data
- Show automation on data pupils collected themselves, manually retrace the generated tree
- Discuss issues with data and problems they generate down the line