21–30 of 44 posts
TensorFlow is admirably easier to install than some other frameworks I thought most frameworks are fairly easy to install in python, usually with a single call to pip. NLTK takes one "pip install nltk" and then "python", "import nltk", "nltk.download()" to download all the corpuses and miscellaneous data. Installing tensorflow seems complicated compared to that. # Ubuntu/Linux 64-bit, CPU only: $ sudo pip install --u…
TensorFlow is admirably easier to install than some other frameworks I thought most frameworks are fairly easy to install in python, usually with a single call to pip. NLTK takes one "pip install nltk" and then "python", "import nltk", "nltk.download()" to download all the corpuses and miscellaneous data. Installing tensorflow seems complicated compared to that. # Ubuntu/Linux 64-bit, CPU only: $ sudo pip install --u…
clearly you haven't tried to install caffe
As I've been reading about tensorflow lately I feel like I'm missing something regarding distributed processing. How can Tensorflow 'scale up' easily if you are outside of Google? We have big datasets that I want to run learning on but it seems awkward to do with tensorflow. We're big enough that the team managing our cluster is separate than development and it is a huge pain if we need them to go install tools on ea…
Their blog post in April mentioned it - https://research.googleblog.com/2016/04/announcing-tensorflo...
That said, I haven't actually attempted any distributed processing, but it looks possible. If anyone has actually tried it and can speak to it I would be curious to what people with experience have to say about it.
As I've been reading about tensorflow lately I feel like I'm missing something regarding distributed processing. How can Tensorflow 'scale up' easily if you are outside of Google? We have big datasets that I want to run learning on but it seems awkward to do with tensorflow. We're big enough that the team managing our cluster is separate than development and it is a huge pain if we need them to go install tools on ea…
No, you're not. Google did this with their build engine (Blaze, internally - Bazel is the open-source API, lacking a distributed build platform). Google are doing this with Apache Beam (the API to Google dataflow) - releasing an API for local testing but not releasing the distributed engine. If you have your data in a Hadoop cluster and are doing image recognition, Yahoo's Cafe on Spark is the only truly distributed…
There's also data parallelism with parameter averaging which we've been doing in deeplearning4j for the last few years. We also support ALOT more than just images. We have the ETL pipelines (kafka etc) to go with it. Watch for a blog post from us on parallel for all (nvidia's blog) where we explain some of this.
I gave a framework agnostic view of the concepts you should consider when looking at distributed deep learning as well:
http://www.slideshare.net/agibsonccc/brief-introduction-to-d...
Wow! I was going to post this but here it is already! I wrote (with a lot of help) the article there. I also jotted down some notes on the process of writing it with O'Reilly in case anybody's interested in that side of things: http://planspace.org/20160619-writing_with_oreilly/
Just a CSS critique: could you add "monospace" to the end of your list of monospaced fonts? I have perfectly good monospaced fonts on this computer, but none of them are Consolas, Menlo, Monaco, or Lucida Console, so I end up with a default proportional serif font.
If the keyword monospace is somewhere in your font stack some browsers use 13px as default font size instead of the usual 16px. The workaround used to be to use both, serif and monospace, in your font stack. This used to work some time ago, I don't know if it still does with contemporary browsers.
Wow! I was going to post this but here it is already! I wrote (with a lot of help) the article there. I also jotted down some notes on the process of writing it with O'Reilly in case anybody's interested in that side of things: http://planspace.org/20160619-writing_with_oreilly/
Just a CSS critique: could you add "monospace" to the end of your list of monospaced fonts? I have perfectly good monospaced fonts on this computer, but none of them are Consolas, Menlo, Monaco, or Lucida Console, so I end up with a default proportional serif font.
Wow! I was going to post this but here it is already! I wrote (with a lot of help) the article there. I also jotted down some notes on the process of writing it with O'Reilly in case anybody's interested in that side of things: http://planspace.org/20160619-writing_with_oreilly/
It was nice to hear a little behind the scenes. I'd be curious to hear any analytics about unique visitors, etc so far from the O'Reilly post if you can share.
Earlier quoted context omitted.
Just a CSS critique: could you add "monospace" to the end of your list of monospaced fonts? I have perfectly good monospaced fonts on this computer, but none of them are Consolas, Menlo, Monaco, or Lucida Console, so I end up with a default proportional serif font.
Thanks for the feedback! I'll pass along to the O'Reilly team and see if there's anything they can do.
Earlier quoted context omitted.
Compared to other frameworks which allow for deep learning, it's much easier to install.
I see. The article was a bit vague for me in what it means by frameworks (does NLTK count? Django? DL frameworks in other languages?), and since I don't know the area too well, that struck out to me as odd. It honestly looks like pretty cool stuff, looking forward to having time to play around with it some day.