barneso

5 ms·

1.
by barneso 10y ago
What I find most exciting here is that they were able to improve performance by combining the three previous steps into one, in other words asking directly for what they wanted rather than trying to trick Hive into executing it in a certain
2.
by barneso 10y ago
That is a good origin story! I would contend though that it was the performing of the data analytics on himself (which helped keep it top of mind and helped him to develop an internal model) that was the most helpful. People simply using
3.
by barneso 10y ago
I'm a diabetic (type one, since 1988) who has also been doing ML startups for the last 15 years. My HbA1C scores have always been below 6, controlled with a two to four blood test per day and long/short (currently Novorapid and L
4.
by barneso 10y ago
Most teams I have seen have either template scripts or boilerplate that generates datasets, and share both the generated data and the scripts via normal ways that people share data and code: disk, S3, github, emailing of notebooks, etc. It
5.
by barneso 10y ago
The Tensorflow code mentions "GCUDACC" in several places, and from the surrounding comments it seems to be targeted at OpenCL as well as CUDA. So it seems that this has been at least considered.
6.
by barneso 10y ago
It's very hard to find a viable business model in machine learning or AI tools and platforms at the moment: the size of the market is small, and most money is being made by the end users. As a result companies who could get value from
7.
by barneso 11y ago
In my experience, the architecture supports cards with shader model >= 3.0. Occasionally a commit will break the support (eg https://bitbucket.org/eigen/eigen/commits/a19653b8035d8ace53... was required ea
8.
by barneso 11y ago
Better, if the processes are on the same machine you could use it to share the data via shared memory or a common memory mapping, to avoid having copies of the data on each end of the pipe.
9.
by barneso 11y ago
Once the open source version of Tensorflow releases multi-node support, this would be one way to make it work. There are potential gains from using a GPU for RF training. As for distributing, in my experience for small models it doesn&#x2
10.
by barneso 11y ago
None of the systems include the data load time, but for mldb and the other non-distributed systems, it's only a few seconds. (edit: my grammar is good not)
11.
by barneso 11y ago
There are plenty of alternatives out there to Spark ML: here is a survey of RF implementations: https://github.com/szilard/benchm-ml/tree/master/z-other-too... There is a whole other world of non stochas
12.
by barneso 11y ago
They do provide some very useful pre-trained models, eg the full parameter set for their Inception model.
13.
by barneso 11y ago
For profiling of models, almost everything needed is already there. You only need to pass in a StepStatsCollector through the Session::Run() method (I called it RunWithStats() ) and hook it up to the Executor Args by filling in this variab
14.
by barneso 11y ago
It seems that MLDB would be a decent fit for this use-case. You would be able to do pre-processing in the background continuously, and predictions could do a significant amount of work on-demand. Depending upon the size of the overall tra
15.
by barneso 11y ago
(Founder here). Could you describe your use-case? This is an interesting question and I'd love to hear more about what you are thinking of. There are two main parts to most machine learning workloads: training and prediction (though i
16.
by barneso 12y ago
I would still expect it to tend towards the normal distribution across a large set of documents. If you model positive and negative word counts as a binomial distribution, you have the the difference of two samples from different binomial
17.
by barneso 12y ago
You are right; this does just shift the bias, which is sometimes all you need (you have a simple algorithm, presumably for a reason). I did misunderstand that you don't have a training set, just a list of positive and negative words.
18.
by barneso 12y ago
Two simple things you could do: 1. Insert each negative example six times into your training set (or weight negative examples accordingly, ie use #positive matches - 6 * #negative matches / (2 * positive word count) as your score 2.