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As someone who dabbles in the field from a strong software engineering background but only MOOCs on deep learning, absolutely this. The courses are often so foc

As someone who dabbles in the field from a strong software engineering background but only MOOCs on deep learning, absolutely this. The courses are often so focused on getting you to reproduce the state of the art you are on rails the whole time with no deep intuition for why things work. And so when I get off the rails and on a real problem or a Kaggle I often find myself training terrible models and not knowing why, getting stuck on bad architectural decisions, or just forging ahead with a terrible dataset without realizing it. I’m reminded of the “draw the rest of the owl” meme. Except these mistakes are more insidious, since everyone can tell if they have drawn an owl, but it can be hard to tell if your model has done something like overfit on minutia until you release it upon the world.

bitL 8y ago
No worries, even PhDs don't understand why things work, only how ;-) You simply need to practice on challenging problems, sometimes ripping off code that is state-of-art, analyzing it, understanding why certain things are done the way they are done, and in a few months you could be able to do your own state-of-art custom models with your own loss functions with distributed training using your own callbacks and getting $500k salary in some valley company.
- commandlinefan 8y ago
  > even PhDs don't understand why things work When you finish your Bachelor's degree, you know everything. When you finish your Master's degree, you realize you know nothing. When you finish your PhD, you realize nobody else knows anything, either.
- flowe_p 8y ago
  what IQ do you think is required for this? do you think a solid lead dev who knows linear algebra and calculus could do this in a year or so?
WalterBright 8y ago
With my first job after college, I found a large impedance mismatch between my (mostly mathematical) education and engineering in the physical world. It took some time to overcome that, but in the end having the strong math basis turned out to be a big advantage. (Better math meant designs could be optimized for lighter weight and reduced manufacturing cost.)
- mattnewton 8y ago
  I think you need both for machine learning systems, and the software engineering one is easier to teach on the job than math education. Or at least we expect to have to grow candidates that way and have infrastructure for it.

bitL 8y ago

No worries, even PhDs don't understand why things work, only how ;-) You simply need to practice on challenging problems, sometimes ripping off code that is state-of-art, analyzing it, understanding why certain things are done the way they are done, and in a few months you could be able to do your own state-of-art custom models with your own loss functions with distributed training using your own callbacks and getting $500k salary in some valley company.

commandlinefan 8y ago
> even PhDs don't understand why things work When you finish your Bachelor's degree, you know everything. When you finish your Master's degree, you realize you know nothing. When you finish your PhD, you realize nobody else knows anything, either.
flowe_p 8y ago
what IQ do you think is required for this? do you think a solid lead dev who knows linear algebra and calculus could do this in a year or so?

WalterBright 8y ago

With my first job after college, I found a large impedance mismatch between my (mostly mathematical) education and engineering in the physical world. It took some time to overcome that, but in the end having the strong math basis turned out to be a big advantage. (Better math meant designs could be optimized for lighter weight and reduced manufacturing cost.)

mattnewton 8y ago
I think you need both for machine learning systems, and the software engineering one is easier to teach on the job than math education. Or at least we expect to have to grow candidates that way and have infrastructure for it.