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Hi shostack, You can try out ThatNeedle API. http://www.thatneedle.com/nlp-api.html . While it is only catering to retail for now. The underlying capability is broader and generic. If you have specific needs, let's get in touch and discuss. You should at least take a look at the gif demo on the site.
That's pretty cool, I'll play around with it. Thanks for sharing!
Just wanted to repost this from the other thread on TensorFlow, since I joined the party a bit late: I think some of the raving that's going on is unwarranted. This is a very nice , very well put together library with a great landing page. It might eventually displace Torch and Theano as the standard toolkits for deep learning. It looks like it might offer performance / portability improvements. But it does not do an…
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I think the fundamental differentiator might be how "production ready" TensorFlow is - the promise of simply declaring an ML pipeline and have that run in very heterogeneous compute environments from mobile phones to GPU blades to plain-old clusters, if fulfilled, can indeed be a huge game changer. The promise is that you literally do not have to write any new code when you are done with a research project / a series…
Torch is used in production by Facebook , deepmind and possibly baidu, amongst others. Facebook especially have released code to make torch run much faster on AWS with GPU cards. Also no startup time. The design is done with a high level language (lua) while computation done mostly in C. I'd be very surprised if tensorflow is actually faster than torch on a single machine
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Only the single machine version is open sourced.
At the moment. They are working on making the distributed version available too. https://github.com/tensorflow/tensorflow/issues/23
Is there anything an "early" programmer like myself can do to play around with this stuff without a background in the related math? I'm dying for this stuff to be dumbed down enough where Joe WebUser can feed in arbitrary data in a csv or point an app at a data source and get some sort of meaningful results. It truly seems like an area where once the barrier to entry is greatly reduced, the creativity of laymen will…
Is there anything an "early" programmer like myself can do to play around with this stuff without a background in the related math? I'm dying for this stuff to be dumbed down enough where Joe WebUser can feed in arbitrary data in a csv or point an app at a data source and get some sort of meaningful results. It truly seems like an area where once the barrier to entry is greatly reduced, the creativity of laymen will…
I found this [2] very approachable, and you can find the material [3] and code [4] from the talks on github.
[1] https://news.ycombinator.com/item?id=10457439 [2] https://www.youtube.com/watch?v=r4bRUvvlaBw [3] https://github.com/scipy-conference/scipy2013_talks [4] https://github.com/jakevdp/sklearn_scipy2013
Jeff Dean has an amazing resume. He designed and implemented MapReduce, BigTable and much more. OT but how much does a super engineer like him get paid at Google?
Largely it'll come down to how much money he wants. His salarly will probably be in the 6-figures, but he'll be a millionaire many times over. He joined Google in 1999 (IPO was in 2004), so his stock will have made him a very rich man.
> ...
> Gradient based machine learning algorithms will benefit from TensorFlow's automatic differentiation capabilities. As a TensorFlow user, you define the computational architecture of your predictive model, combine that with your objective function, and just add data -- TensorFlow handles computing the derivatives for you.
Interesting, it kind of look like the machine learning focused version of NASA's OpenMDAO (also a graph-based analysis and optimization framework with derivatives, but for engineering design).
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Torch is used in production by Facebook , deepmind and possibly baidu, amongst others. Facebook especially have released code to make torch run much faster on AWS with GPU cards. Also no startup time. The design is done with a high level language (lua) while computation done mostly in C. I'd be very surprised if tensorflow is actually faster than torch on a single machine
Torch has extremely difficult learning curve due to Lua. With Tensor Flow underlying engine in C++ it is likely as efficient as Torch. Special extension such as Nvidia Cudnn could also be used with tensorflow.
Or perhaps you mean to say that Torch itself is difficult to learn because of design choices that were made in order to use Lua?
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Largely it'll come down to how much money he wants. His salarly will probably be in the 6-figures, but he'll be a millionaire many times over. He joined Google in 1999 (IPO was in 2004), so his stock will have made him a very rich man.
Way off. He's a Senior Google Fellow and his bio used to be on the executive leadership page of Google (I can't find that page any more). He is being paid on the order of 10MM per year, base, easily.
Executive compensation is a very odd area, as I said, it pretty much depends how much money he wants.