Data Community DC is pleased to announce a new service to the area data community: topic-specific discussion lists! In this way we hope to extend the successes of our Meetups and workshops by providing a way for groups of local people with similar interests to maintain contact and have ongoing discussions. Our first discussion list will be on the topic of Deep Learning. The below is a guest post from John Kaufhold. Dr. Kaufhold is a data scientist and managing partner of Deep Learning Analytics, a data science company based in Arlington, VA. He presented an introduction to Deep Learning at the March Data Science DC Meetup.
A while back, there was this blog post about Deep Learning. At the end, we asked readers about their interest in hands-on Deep Learning tutorials.
The results are in, and the survey went to 11. And as in all data science, context matters–and this eleven is decidedly less inspiring than Nigel Tufnel’s eleven. That said, ten out of eleven respondents wanted a hands-on Deep Learning tutorial, and eight respondents said they would register for a tutorial even if it required hardware approval or enrollment in a hardware tutorial. But interest in practical hands-on Deep Learning workshops appears to be highly nonuniform. One respondent said they’d drive from hundreds of miles away for these workshops, but of the 3000+ data scientists in DC’s data and analytics community, presumably more local, only eleven total responded with interest.
In short, the survey was a bust.
So it’s still not clear what the area data community wants out of Deep Learning, if anything, but since April I’ve gotten plenty of questions from plenty of people about Deep Learning on everything from hardware to parameter tuning, so I know there’s more interest than what we got back on the survey. Since a lot of these questions are probably shared, a discussion list might help us figure out how we can best help the most members get started in Deep Learning.
So how about a Deep Learning discussion list? If you’re a local and want to talk about Deep Learning, sign up here:
For the record, this discussion list was Harlan’s original suggestion. If you’re looking to take away any rules of thumb here, a simple one is “just agree with whatever Harlan says.” Tommy Jones and I will run this discussion list for now. To be clear, this list caters to the specific Deep Learning interests of data enthusiasts in the DC area. For a bigger community, there’s always deeplearning.net, the Deep Learning google+ page , and individual mailing lists and git repos for specific Deep Learning codebases, like Caffe, pylearn2, and Torch7.
In the meantime, I was happy to see some Deep Learning interest at DC NLP’s Open Mic night by Christo Kirov. And NLP data scientists need not watch Deep Learning developments from the sidelines anymore; some recent motivating results in the NLP space have been summarized in a tutorial by Richard Socher. I’m not qualified to say whether these are the kind of historic breakthroughs we’ve recently seen in speech recognition and object recognition, but it’s worth taking a look at what’s happening out there.
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