eval parse (@evalparse) 's Twitter Profile
eval parse

@evalparse

Daddy. Risk modeller. #julialang #rstats Python machine learning developer. Creator of #diskframe.

ID: 71234717

linkhttps://www.youtube.com/channel/UCKxHtRdtFEPihEjTtjG8Y8w calendar_today03-09-2009 11:56:04

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eval parse (@evalparse) 's Twitter Profile Photo

Nerd alert: someone's written a book on recursion. Yes, just this one concept! I think a book on delimited continuations (which I still can't understand) is needed. Akerman's Function is quite doable without recursion. Just use a stack. nostarch.com/recursive-book…

Naty Clementi (@ncclementi) 's Twitter Profile Photo

After 1 year at Voltron Data, I found myself along with my teammates and 50+ people in need of a job I have plenty experience building OSS Python data tools, presenting at conferences and OS community management. If you think I'd be a good fit for a job please let me know

eval parse (@evalparse) 's Twitter Profile Photo

roc-lang.org/fast I love how Roc (@roclang) and Richard Feldman (and co?) writes the explainers. He explained that fast in Roc means faster than GC languages but as fast as langs that can (potentially) have mem unsafe code. This shows awareness, breadth and dept of knowledge

eval parse (@evalparse) 's Twitter Profile Photo

Interfaces is coming to Julia? This doc points to a glimmer of hope! Formally enforced interfaces for Arrays etc will be super awesome! hackmd.io/BbEw0_B4Q8uDSS… Traits too could be on the cards.

eval parse (@evalparse) 's Twitter Profile Photo

I had a bit more time on hand now and have started to develop JLBoost again. What struck me was how fun it was to develop in Julia. It’s more addictive than video games. Also how Julia is battling more than the 2 language problem. It’s battling the good enough problem.

eval parse (@evalparse) 's Twitter Profile Photo

It’s a free market. If u can get better quality at cheaper price why not? This is not discriminating against non-Indians. It’s Indians winning fair and square in a market place!

eval parse (@evalparse) 's Twitter Profile Photo

An idea came to me about GBM algos like XGBoost: What if I add a few randomly generated features to a tabular dataframe and call XGBoost on it. I stop the growth of a node if a random feature is selected and use that as my stop-growth criterion. Is this a known approach?