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?
You keep making these very negative posts, where you sound very confident, but resort to either word salad or making things up.
> 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.
For structured data this is valid, but the power of deep learning is for unstructured data where the embeddings and features need to be learned from raw data
Depends on the task. NLP? Audio? Video? DL is probably best. Classification, regression etc.? Don't bother (in my experience). You can still utilize deep learning-esque tools like embeddings (which aren't deep learning at all) and put an SVM on top.
That will almost always be the case. If basic stats could solve a problem then they still can.
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.
Lots of organizations that are a bit clueless and trying to catch up quickly, driven by hype, think they need deep learning when they actually need Bayesian learning.
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.
If you dont have a lot of data, then statistical learning. If your data is structured and have well defined interpretations, then statistical learning.
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.
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.