31–40 of 71 posts
FYI, Jeff Dean is the inventor of most of Google's distributed computing infrastructure including MapReduce. Definitely up there with the likes of John Carmack and Fabrice Bellard as one of the great software engineers of all time.
The Jeff Dean Facts are worth reading: https://www.quora.com/What-are-all-the-Jeff-Dean-facts Personal favorite: "Jeff Dean once shifted a bit so hard, it ended up on another computer."
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…
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?
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.
Yay!! An open sourced voice engine in the future? That would really shake things up.
a) Stuff similar to this has been available for ages and there are no (good) open source voice recognition packages.
b) It requires absolute mountains of training data which we don't have.
c) It requires designing a suitable network, which I'm not sure if we have, but I would doubt it.
d) It requires training a network on the mountains of training data using an immense computing cluster, which we requires money that we don't have.
Don't hold your breath.
FYI, Jeff Dean is the inventor of most of Google's distributed computing infrastructure including MapReduce. Definitely up there with the likes of John Carmack and Fabrice Bellard as one of the great software engineers of all time.
The Jeff Dean Facts are worth reading: https://www.quora.com/What-are-all-the-Jeff-Dean-facts Personal favorite: "Jeff Dean once shifted a bit so hard, it ended up on another computer."
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Well, it looks way more scalable than Theano or Torch while being as easy to use as Theano. I'd say that's pretty exciting considering the number of groups working on way lower-level scalable neural nets. This is "not a game-changer" in the same way map-reduce isn't a game-changer wrt for loops. Also check out TensorBoard, their visualization tool (animation halfway down the page): http://googleresearch.blogspot.com/…
Only the single machine version is open sourced.
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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…
That's not really a fundamental differentiator. Torch/Theano are definitely production ready. I think the portability is definitely an advantage, though.
Maybe it would work good enough in a service backend. But even there it would not scale that well. For example, it doesn't support multi-threading (running a theano.function from multiple threads at the same time).
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I'm dying for this stuff to be dumbed down enough[...] It kind of already is. Have you read the docs/examples? I don't think your mentality is fruitful. Having argued with people who shared your point of view, it seems there will always be something too difficult that prevents them from being good at X. There's no substitute for sweat. Have fun with the code they gave you and see where you end up!
I think we have different definitions of what "dumbed down" means. I never said I could/would never put in the work to learn it. I'm saying that the place it is in right now is still too advanced for someone with my background to pick up and play around with without sitting down to seriously study the underlying concepts that are objectively fairly dense subject matter that can require advanced math and CS background…
The command line make command on GNU/Linux is an example of something that "dumbs down"/makes easy a quick start , as opposed to editing and configuring the Makefile yourself. Similarly, yum/apt-get take this "dumb down" one step further.
Nothing wrong with removing friction. Infact this is an idea for a startup right here, remove friction from machine learning/NLP/API.
That is why I responded to shostack in the first place. The response was specific to his question and I got plenty of downvotes on my karma. No worries there :)
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'd suggest http://karpathy.github.io/2015/05/21/rnn-effectiveness/ is a good place to start.
The other option is using nVidia Digits toolkit.