111–120 of 211 posts
This looks similar to Orange. Orange has a visual designer, easy data access (SQL, arbitrary files) and lots of widgets, but it's a desktop tool not a library and doesn't standardize the data structures. http://orange.biolab.si/ http://docs.orange.biolab.si/widgets/rst/index.html
Earlier quoted context omitted.
Google is a big place, with diverse opinions. Even better, it encourages updating one's opinion in face of new evidence. GPUs have proven themselves cost-effective in tackling a number of vision problems [and others], Google has those problems, so the opinion was updated and the problems were solved in a cost-effective manner.
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...
Are there any major conceptual differences to Theano? Not that I wouldn't appreciate a more polished, well funded competitor in the same space. It looks like using TensorFlow from Python will feel quite familiar to a Theano user, starting with the separation of graph building and graph running, but also down into the details of how variables, inputs and 'givens' (called feed dicts in tensorflow) are handled.
I'm looking at the RNN implementation right now ( https://github.com/tensorflow/tensorflow/blob/master/tensorf... ). It looks like the loop over the time frames is actually in Python itself. for time, input_ in enumerate(inputs): ... This confuses me a bit. Maybe the shape is not symbolic but must be fixed. I also haven't seen some theano.scan equivalent. Which is not needed in many cases when you know the shape in a…
That said, you can do a lot with truncated BPTT and LSTM. See the sequence modeling tutorial on tensorflow.org for more details.
i really wish people would stop calling multidimensional arrays tensors. there is already well defined language for this. multidimensional array is fine, psuedotensor is fine. tensor is confusing if you have any previous background with tensor calculus before the word became machine learning flavour of the month. still. this does look pretty cool overall. processing large volumes of data is becoming increasingly less…
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Might one conclude that getting hung up on one opinion from a large group of people projected forward in time is... not very useful?
Might one conclude that this influential opinion might have created a big old stinky career mess for someone in the field that took many months to clean up and cost one a fair chunk of change to do so? So perhaps one could be forgiven for noting such a disruptive change in viewpoint? Now we all must own our choices and one was very silly, stupid, and naive to blindly accept a position at Google, but everyone makes mi…
That sucks, but that kind of thing happens a lot. It's not that surprising. Google wanted a homogeneous infrastructure for a long time. But a new application (neural networks) motivated a new infrastructure (GPUs in clusters).
There was that Stanford paper everyone talks about which compared training a neural network on Google's 16K cores (DistBelief paper) vs a handful of GPUs with infiniband. Even Andrew Ng has seemed to subtly criticize Google for thinking "the old way" (i.e. "cloud" technology vs HPC technology, HPC being more effective for neural nets)
Also, I don't quite understand your comment about brawny vs. wimpy. Yes Urs wrote a rebuttal against wimpy cores. As far as I remember, his argument is basically that there is some portion of software on the critical latency path that's not multithreaded. wimpy cores make the most sense in a world of perfect parallelism.
http://googleresearch.blogspot.com/2015/11/tensorflow-google...
This 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).
From the AMS statement on The Culture of Research and Scholarship in Mathematics: "For this reason, mathematicians traditionally list authors on joint papers in alphabetical order. An analysis of journal articles with at least one U.S. based author shows that nearly half were jointly authored. Of these, more than 75% listed the authors in alphabetical order. In pure mathematics, nearly all joint papers (over 90%) list authors alphabetically."
http://www.ams.org/profession/leaders/culture/CultureStateme...
"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-model-for-d...