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TensorFlow: open-source library for machine intelligence
141–150 of 211 posts
Re: TensorFlow: open-source library for machine intelligence
#142Interesting! I wrote in a draft post back in late 2013 [1], that asked: "What if one could have a fully declarative “matrix language” in which all data transformations ever needed could be declaratively defined in a way that is very easy to comprehend?" I'm now pondering whether TensorFlow isn't quite an answer to this question? [1] Posted the draft now for reference: http://bionics.it/posts/matrix-transformation-as-…
The idea of representing a program by a declarative computation graph of matrix transformations has been big in deep learning research for a few years. Theano [1] is the canonical example, though more recently the space has gotten bigger with other frameworks including CGT [2] and now TensorFlow. The computational graph abstraction is really nice in part because it gives you very straightforward automatic differentia…
Thanks! Yea, though in fact my idea is more about wiring the actual (matrix/tensor) operation with dataflow, rather than just the dataflow between such operations.
But it might be bit of a different problem area :)
Re: TensorFlow: open-source library for machine intelligence
#143what would be really awesome is if Andrew Ng or Norvig build a course around Tensorflow. It is really not useful to a beginner to be learning everything in matlab.
My first thought when reading the docs was: this is a game changer for the efficiency of applied ML (nlp, vision, speech) phd students. Coming from a school where there's a bit of a libertarian "write it all yourself, from scratch!" ethos, I always marveled at how much mileage other students got from research groups that built off a common codebase. Exciting to start to see glimmers of that possibility across the entire field.
Re: TensorFlow: open-source library for machine intelligence
#144Geoffrey Hinton and Jeff Dean will give separate tutorials at NIPS. https://nips.cc/Conferences/2015/Schedule?day=0
Re: TensorFlow: open-source library for machine intelligence
#145Earlier quoted context omitted.
What is Nile? I can't find it on Google.
Nile is a language built by Dan Amelang, a researcher @ viewpoints research institute. It is a language made for graphics rendering, it has the goal of having no incidental complexity. 2D vector graphics rendering as an example, takes ~10.000 lines of code using traditional libraries like Cairo (used by Firefox) or Skia (used by Android and Chrome). In Nile, it's 200 lines of code. Check out this repo, and the pdf in…
And check out the Nile Viewer as well!
http://subtlegradient.github.io/nile/viz/NileViewer/demo_sha...
Re: TensorFlow: open-source library for machine intelligence
#146I can't wait to try this tonight. I have a fun "messing around" project (I am using GnuGo to generate as much training data as I need for playing Go on a very small board, and I am almost to the point of starting to train and test models). BTW, Ruby has always been my scripting language but because of the wealth of libraries for deep learning I am thinking of switching to Python.
I have started to experiment with it. Really awesome documentation and setup nstructions (using Ubuntu).
Re: TensorFlow: open-source library for machine intelligence
#147Earlier quoted context omitted.
That... Was not... My experience there... But kudos to the deep learning guys for overcoming that potential energy barrier NVIDIA couldn't surmount on their own...
To me, processing lots and lots of data with "relatively simple" algorithms spells GPU, pretty much. Machine learning seemed destined to bump into GPUs sooner or later.
Why yes...
Re: TensorFlow: open-source library for machine intelligence
#148Earlier quoted context omitted.
hmmm. Google does not seem to agree. Your assertion is a wish not a fact. Dogmatic 3.x people are being shown, yet again, the uncomfortable, brutal truth, that 3.x is not happening in science. Seriously. If you're advising someone considering Python on the 2 v 3 question today, how can you not tell them that possibly one of the most important new machine learning libraries, only works on 2.7.
First off, 3.x is happening in science. I'm a scientist, and I use 3.x, and as of this fall, every single scientific package that I use has been ported to 3.x. Using @ for matrix multiplication is also a pretty big deal to me. It's worth remembering that most scientists don't do any machine learning, because it's not all that useful in many domains. A lot of science is trying to find an explanatory model for the obse…
Yes - I have also moved (a holdout library was ported recently). I even liked it. But here I am thinking about moving backward again after having crossed that rubicon.
Three's problem is that even if 95% of stuff is on it, basically 100% of stuff is on 2.7, including brand new stuff. Let's say you use 10 libraries on average and 95% of libraries have been ported. The odds of having one library missing are still 40%! (1-0.95^10) This ratio goes up the more specialised your work, or the greater your investment in legacy, which is why most 3.x people are generalists/web where they cannot understand the 2.x position. For me this Tensorflow library transports me right back into exactly that 40% problem. That problem does not exist the other way around unless you at all costs need to use Asyncio instead of something that's already been there for years.
In my case I'm going to persevere and put 2.7 back into a virtualenv or something but it's really not ideal and my point is still that if 3.x gave science/engineering people something properly compelling (@ operator is not enough in my view) then the above problem would not be happening.
For example a difficult-but-possible megawin would be to put GPU computing natively into Python. GPU is more than 50% of the compute silicon in most computers nowadays (Iris 6200 uses up 50% of die size in recent Intel core i7). If you include multicore and discrete GPU then standard Python is only targeting about 10% of the available compute silicon in a modern PC! Imagine how very rapidly everybody would move if there were a GPU-enabled Numpy array in 3.6.
Re: TensorFlow: open-source library for machine intelligence
#149I'm kinda floored, by this. Have been developing a flow based language to do this. Even started with the approach of using Python and then moving to scripting directly in C++. Maybe I tweak my own project. Anyone playing with low level GPU deployments, like Metal? [EDIT] Ok, so there is some GPU - should probably play some more before asking obvious questions. Really keen on scripting out flow directly.
link to your project ?
Sorry, not ready for prime-time. Closest is a visualization of the previous version of the script: https://www.youtube.com/watch?v=a703TTbxghc
That version wasn't a NN graph for ML; more of an executable flow graph for visual music. Will post something on https://github.com/musesum in a couple months.
Re: TensorFlow: open-source library for machine intelligence
#150This is really significant. At this moment in history, the growth of computer power has made a bunch of important signal-processing and statistical tasks just feasible, so we are seeing things like self-driving cars, superhuman image recognition, and so on. But it's been very difficult to take advantage of the available computational power, because it's in the form of GPUs and clusters. TensorFlow is a library design…
One small note on the paper itself. I love that the paper's author are in alphabetical orders, without any stupid jostling over first/last authorship. I think that's fantastic and wish academia went that way too (yeah right).
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