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I would almost state "Clean datasets (like MNIST) considered harmful" because without exception the data scientists I meet who work with them just shovel real d

by varelse 11y ago

I would almost state "Clean datasets (like MNIST) considered harmful" because without exception the data scientists I meet who work with them just shovel real data blindly into ML code and kvetch when they don't beat simpler existing approaches right out of the gate. And without fail so far, just cleaning up the data (feature-scaling, removing input errors etc) changes this. I vacillate on building an automagic tool to do this for them, but IMO such a tool would send tsunamis of technical debt up their data pipelines as they got even sloppier about data until the tool was overwhelmed by stuff it couldn't detect. I'd much rather handle the lesser of two evils of them getting nowhere until they realize this isn't magic.

Teodolfo 11y ago
Would you write something for biologists entitled "working on Drosophila considered harmful"?
- varelse 11y ago
  Drosophila is a real noisy living system, and as a biologist myself, and unlike the data scientists with whom I work, none of us would consider the fruit fly to be an approximation of a human being. Further, the biologists and chemists I know are aware of their limitations and do their best to surpass them. The data scientists I work with OTOH constantly tell me how code should be written despite knowing little more than single-threaded Python and Hadoop. See also their contempt for code maintenance and engineering or "ops." Of course, this is an unfair strawman to the smart ones who aren't this way, but I'm seeing more and more of this as time goes on, not less. Perfect example: the myriad of supposed improvements to neural network training for which only MNIST results are ever shown. Wouldn't it be great if there were a 6+ TFLOP processor available along with an open source specification for and multiple vendors providing servers with 8 of these wonderful gadgets that could be used to train, say AlexNet, with this new and improved algorithm? But that would require ops(tm) and ops is for engineers, not data scientists. Or TLDR: they did try it and it lost to Nesterov Momentum or something similar on larger systems (results not shown).

by varelse 11y ago

Teodolfo 11y ago

Would you write something for biologists entitled "working on Drosophila considered harmful"?

varelse 11y ago
Drosophila is a real noisy living system, and as a biologist myself, and unlike the data scientists with whom I work, none of us would consider the fruit fly to be an approximation of a human being. Further, the biologists and chemists I know are aware of their limitations and do their best to surpass them. The data scientists I work with OTOH constantly tell me how code should be written despite knowing little more than single-threaded Python and Hadoop. See also their contempt for code maintenance and engineering or "ops." Of course, this is an unfair strawman to the smart ones who aren't this way, but I'm seeing more and more of this as time goes on, not less. Perfect example: the myriad of supposed improvements to neural network training for which only MNIST results are ever shown. Wouldn't it be great if there were a 6+ TFLOP processor available along with an open source specification for and multiple vendors providing servers with 8 of these wonderful gadgets that could be used to train, say AlexNet, with this new and improved algorithm? But that would require ops(tm) and ops is for engineers, not data scientists. Or TLDR: they did try it and it lost to Nesterov Momentum or something similar on larger systems (results not shown).