I think slide 12 touches on this. Even in the case of an image we can process it pixel by pixel, but that would be lunacy!
For text great results have been achieved using automatons, but they only work for structured strings and break if you add only a little bit of noise.
I feel like ML should be considered whenever you feel like programming something requires you to deal with many different cases, you have a lot of example data available, and having some false positives / true negatives is not a big problem.
For text great results have been achieved using automatons, but they only work for structured strings and break if you add only a little bit of noise.
I feel like ML should be considered whenever you feel like programming something requires you to deal with many different cases, you have a lot of example data available, and having some false positives / true negatives is not a big problem.