
A JavaScript deep learning and reinforcement learning library - sdomino
https://github.com/janhuenermann/neurojs
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merricksb
Discussed 2 days ago (244 points):

[https://news.ycombinator.com/item?id=13742911](https://news.ycombinator.com/item?id=13742911)

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solomatov
It's useful only for predictions. Unless, GPU (Possibly with WebCL) support
will appear, it will be impractical to use it for training.

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make3
even for predictions, you need the model (what was learned in the training
phase). deep learning models are of multiple gigs in size. so, in browser
wouldn't be practical, except for toy stuff.

sending the input data to the server, doing the computations there and getting
the answers back will be the only practical way to go for remotely serious
applications for a while still

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Scea91
And you would also have to make the model available to the client, which is
not reasonable in many commercial applications.

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platz
I get why deep learning is nice but I just do not get the hype around
reinforcement learning yet. RL seems great for things like training video game
agents and have seen videos of this, but fail to understand where RL can be
applied in the real world.

It reminds me a bit of genetic algorithms. GA is the 'last resort' when you
truly know nothing about how to model your problem.

What is the sweet spot for RL?

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tikhonj
I work in supply chain optimization, and reinforcement learning has been an
important technique in the field for _decades_. Supply chain problems are
naturally modeled as Markov decision problems (MDPs). As the state space of
your MDPs gets bigger and bigger, simulation-based reinforcement learning
becomes one of the most versatile techniques for approximating optimal
solutions.

I see some sort of reinforcement learning as the most promising technique for
overcoming the dramatically named "curse of dimensionality" in the state—the
single biggest roadblock to optimizing more complex supply chain models.

In fact, the study of MDPs and their solutions stems from operations research,
and I think studying problems in that context give you a powerful way of
understanding how reinforcement learning algorithms work. Basic inventory
control problems are very intuitive, and there's a natural progression from
exact dynamic programming methods (Bellman iteration and policy iteration) to
different reinforcement learning algorithms that really helps build an
intuition for how RL works.

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sabertoothed
I am working in that niche as well - probably even in a special niche of that
niche.

Could I get in touch with you via PM?

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albertTJames
Interesting work ! Although I am not learning that :) What would be really
interesting is a common interface for the browser and node, the former using
this library, and the latter using a node extension communicating with
tensorFlow... And on the user side a syntax EXACTLY similar to the Keras
syntax. That - would be the bomb.

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make3
most deep learning models are of multiple gigs in size. so, in browser
wouldn't be practical, except for toy stuff.

sending the input data to the server, doing the computations there and getting
the answers back will be the only practical way to go for remotely serious
applications for a while still

~~~
albertTJames
CNN models with a fair amount of variables (1million) are in megs. Learning is
another story, and could be done on the server, but prediction is totally
feasible on the browser.

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fenomas
Does anyone know enough to lend some context here? I gather this works
similarly to ConvNetJS, or solves some of the same problems. Does it go
further, or work in a different way, or what?

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vonnik
Just curious: Where does the computation happen? Is it all Javascript?

