[scryder]

>Every time I've heard from someone who has switched from a manual feature engineering approach to deep learning I've heard the same results ... dramatic improvements in accuracy, generally within a few days of work

It bothers me how often otherwise rational individuals continue to be susceptible to survivorship bias.

Of course you'd only hear about the successes, the failures are either embarrassed or know better than to tell you their story.

The people who tried it for several months, saw accuracy cap out at something like 60-70% for processes that need something like >90% confidence to justify expenses, and which proceed to get ignominiously shifted into a different team (or fired) for building what the higher-ups view as a massive waste of money at your salary and the other engineers see as a data-guzzling black box. No, these guys aren't likely to tell their story. Or, at least THIS story.

The story you instead get from these guys is how

>After having had some fun times doing deep learning and data analysis, I'm excited get into the big new field of $THING_THAT_WAS_HIRING

and all the other dross that modern vocal programmers use to mask any possible scent of failure and actual on-the-job difficulty experienced.

[jacquesm]

This goes pretty much for every tool though. Applying a tool to a job it isn't well suited to is always going to come out as a frustrating experience, be it a hammer or a piece of software.

What I always try to do is to get a feel for the problem space trying different methods with as little investment as possible. That way you will - once you decide to go full power after a certain solution method - at least have a feeling that you are on the right path.

Dogmatically trying to shoehorn every problem into the toolset that you know how to use is a way to stay reasonably productive but it rarely leads to optimal outcomes, sometimes you simply have to learn how to use a new tool in order to get to the maximum.

I'm the last person to jump on new bandwagons, still have a dumb phone, don't use facebook and still run my own mailserver. Even so, when a tool has a significant and most importantly measurable advantage compared to the tools I'm already familiar with I'll adapt.

[mcv]

I see plenty of stories about "$POPULAR_THING is not the solution and here's why". If it's true that HN has been hating on Deep Learning for the past year, there should be a market for articles explaining why or when it doesn't work. If someone started out using Deep Learning and then switched to something else, that seems like an excellent subject for an article.