As of a couple of years ago the main sentiment was: deep learning is neat but your problem could be solved by statistical learning techniques so check those out first. Does this still hold up?
Of the 10% that can't be solved that way, 90% are solved with data cleaning + a linear model.
Of the 1% that can't be solved either way, 90% are solved with other statistical techniques (timeseries modeling, decision trees and so on).
For the remaining .1%, sure, deep learning I guess.
> not fucking up your data, modeling your tables correctly, and a SQL query
And getting a pony.
> Of the 10% that can't be solved that way, 90% are solved with data cleaning + a linear model.
87% of statistics are made up.
> Of the 1% that can't be solved either way, 90% are solved with other statistical techniques (timeseries modeling, decision trees and so on).
But why?
This reminds me of how some people in the early '80s sneered at people who did their calculations using computers - recommending instead to memorise a billion mathematical shortcuts that would take longer to learn than programming a computer.
> 87% of statistics are made up.
Yes, of course, I didn't mean "lower integer part of nine tenths of the total number of problems". Did that really need to be specified?
>> Of the 1% that can't be solved either way, 90% are solved with other statistical techniques (timeseries modeling, decision trees and so on).
> But why?
Is it really controversial that you should go for the simplest model that works?
I guarantee that 90% of the things I want to do have nothing to do with a table lookup.
Select * from images where color='red' and item='light' and tag='traffic'.
Select * from voice where token='brrrrr'
All this AI development may open up new business opportunities or close existing ones - but for existing businesses, assuming they survive the disruption caused by AI, they probably still would be best served by focusing on getting some basic stats used in their processes. If they can't do that, then a neural network isn't going to make the situation any better for them.
Instead of looking at stuff from Murphy volume I, they should look at volume II or Gelman's books.
There are neat combinations of ideas from both fields. Aside from volume II, Pyro's documentation provides some interesting use cases.
So if you have lots of data and no reasonable way to extract information or process them, you go to DL stuff.
You can get a lot of mileage out of gradient boosted trees and other forms of ensembles.