Follow the Data

A data driven blog

Archive for the tag “Python”

Two good resources (about sklearn and deep learning)

I have been using R, mostly happily, for the past 6 or 7 years, for its variety of statistical and machine learning packages and the relative ease of producing nice-looking plots. At the same time I am a big user of Python for things that R really doesn’t do that well, such as large-scale string manipulation. I had been aware of scikit-learn (or sklearn) for a while as a potential way to be able to do “everything” in Python including stats and plotting, but never really felt the pull to start using it. In the beginning, it felt too immature; later, it felt too messy when I looked at the documentation.

Last week, however, I came across a really good tutorial by Jake Vanderplas that finally made sklearn click for me and perhaps will push me over the edge to start using it. (I don’t expect to leave R any time soon, though…) The tutorial shows, step by step, how to divide your data set into training and test sets, fit models and make predictions, perform grid searches for parameter settings, plot learning curves etc.

┬áDeep learning is another subject (although much bigger than sklearn of course) that I have kept up a passing interest in but never really looked into properly, because I wasn’t sure where to start. The new book Deep learning: Methods and applications (PDF link) by Li Deng and Dong Yu seems like a good place to start. I’ve only read a few chapters, but so far it has done a good job of clarifying terms and putting deep learning methods into a historical context.

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This & that

  • The BigML blog has been on a roll lately with many interesting posts. I particularly liked this one, Bedtime for Boosting, which goes pretty deep into benchmarking various versions of the boosting algorithms we all know and love (?).
  • Mark Gerstein of Yale University has a nice slide deck about the big data blizzard in genomics (<– pdf link). There are lots of ideas here about how to build predictive models based on, for example, ENCODE data. I won’t get into the ongoing controversy around ENCODE here, suffice to say that I think the ENCODE data sets are a good resource for starting to build statistical models of genomic regulation on a larger scale.
  • The O’Reilly Radar has a good post about how Python data tools just keep getting better.
  • An “ultra-tricky” bioinformatics challenge will be run by Genome Biology on DNA Day (April 25), with a “truly awesome” prize. Intriguing.

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