You can probably use deep learning even if you don't have a lot of data
Andrew Beam's post offered very persuasive evidence that Jeff Leek's intuition (deep learning yields poor performance with small sample sizes) is incorrect. The error bars and the consistent trend of higher accuracy with a properly implemented deep learning model, particularly with smaller sample sizes, is devastating to Leek's original post.<p>I think this is a fantastic example of the speed and self-correcting nature of science in the internet-age.<p>As an aside, @simplystats blocked me on Twitter, which I assume is in response to this tweet: <a href="https://twitter.com/ErickRScott/status/871586233599893505" rel="nofollow">https://twitter.com/ErickRScott/status/871586233599893505</a>
 and it seems that I'm likely not the only one blocked: <a href="https://twitter.com/jtleek/status/871693250947624961" rel="nofollow">https://twitter.com/jtleek/status/871693250947624961</a><p>What's most concerning about @simplystats blocking activity is the chilling effect it has on discourse between differing perspectives. I've tried to come up with a rationale for why highlighting the most recent evidence in reply to someone who sympathized with Leek's original post (btw, @thomasp85 liked the tweet) is grounds for blocking , but I can't come up with a reasonable idea.<p>Further aside, is irq11 Rafael Irizarry?<p>Update: after emailing the members of @simplystats they have removed the block on my account and offered a reasonable explanation. SimplyStats is a force for good in the world (<a href="https://simplystatistics.org/courses/" rel="nofollow">https://simplystatistics.org/courses/</a>) and I look forward to their future contributions.