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> While a very expressive and extendable language like Python seems well suited to researching the desired abstractions when designing neural nets and ML paradigms, it seems to me that a production phase would benefit from translating it to a 'sleek' language like Go — which forces you to think closer to the machine, and opens boulevards to safe performance. > > I wonder what people fluent in Go and other languages, experienced with ML, think of this. (it's a minor concern overall, but the future of Go in ML is of interest to me)

I gave this talk: https://speakerdeck.com/chewxy/data-science-in-go, I wrote this cheatsheet: https://www.cheatography.com/chewxy/cheat-sheets/data-scienc...

I wrote Gorgonia (https://gorgonia.org). I wrote a variant of AlphaGo in Go (https://www.youtube.com/watch?v=nk87zsxpF1A).

So, to answer your question, yes you are ABSOLUTELY correct in that intuition. I speak several languages - in the data world: R, Python, Julia; In the logic world: Haskell and Prolog (Datalog); In the software engineering world: C, Go, Rust. Go sits right in the middle of all this. From my point of view, it's the right balance and is Thanos' preferred language too.

On that note, Gorgonia has seemingly recently took off thanks to the heroic efforts of the community - this year there were talks not by me on deep learning in Go using Gorgonia and that kinda made me quite happy.

⬐ K0SM0S

I'm definitely going to review your work. You/it seem/s pleasingly enthusiast and just brilliant!

The names you mention, from Haskell to Rust passing by Julia... if Go indeed "is the right balance"... well it really makes sense based on what Pike, Ken, Russ and others said/say, and my related parts in my 20+ years of general computer nerding.

(currently listening to your YouTube talk and loving it)

⬐ chewxy

Thank you. :)

I usually have massive nerves when I give talks. I once remarked how average Go is: https://blog.chewxy.com/2019/02/20/go-is-average/

⬐ franciscrick1

This is a great talk.