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Comment by z1mm32m4n | Hacker News Reader

I think my favorite part of this is that inevitably people will write type definitions for TensorFlow.js in TypeScript, Flow, ReasonML, Purescript, and more.

It has always seemed like data science and machine learning tasks have always been most popular in dynamic languages like Python, Julia, and R. I really hope this can be a bridge over to typed machine learning APIs!

(Of course, I’m also interested to hear about your favorite typed machine learning library if you have some that you already use)

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> I think my favorite part of this is that inevitably people will write type definitions for TensorFlow.js in TypeScript

Actually, it already ships with typescript definitions - it's written in TypeScript![1]

[1] https://github.com/tensorflow/tfjs/blob/master/src/index.ts

Absolutely spot on... I love watching Siraj Raval's videos on ML - but darn it every other video has a 2 or 3 minute segment where he's just scratching his head - finally to realize there is a typo that could have been caught with a typed language!

"favorite typed machine learning library"

Flux is essentially nothing but some clever use of Julia's type system:

https://github.com/FluxML/Flux.jl

There's some ability to generate javascript too, which you can try here: http://fluxml.ai/experiments/mnist/ . Edit: I see it's actually using deeplearn.js == TensorFlow.js for this, details at https://github.com/FluxML/FluxJS.jl

I am starting to really like Javascript now that Typescript showed up. Makes things so much nicer.

Neural net layers should have been statically typed ages ago, as anyone who has struggled with mismatching array sizes in Python already knows.

I'm really looking forward to a superior dev experience with writing ML code once great autocompletion kicks in

I would love to see some full Tensor Flow bindings for Go. It's concurrency system is great for data science tasks. The statically linked binary makes it great for distributed workflows.

How about TensorFlow for Swift? :-D

https://news.ycombinator.com/item?id=16719615

I think dynamic languages are a bad fit, because they force the programmer to think in data-flow graphs, whereas a more static language could allow for more offloading of complexity to a compiler (i.e. let the compiler build the data-flow graph, which is essentially what we've been doing for decades).

A good set of Haskell bindings to the TensorFlow library:

https://github.com/tensorflow/haskell

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