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I think it’s unlikely to become expert in all of those things. If you do, it’s over the course of an entire career, not to get started. I guess it comes down to
by agentofoblivion 8y ago
I think it’s unlikely to become expert in all of those things. If you do, it’s over the course of an entire career, not to get started. I guess it comes down to how much expertise is “enough” for you. Naturally, if you split your time across 3 domains you won’t be as expert as someone who dedicated all their time to going deep in one.
In the context of a big company, I think it makes sense to have a specialized workforce. Why look for the one in a billion person that can publish top quality theoretic papers and then implement them on distributed gpus in an optimal way while also building simple Random Forest models for your business? I’d rather that person do more of the most valuable thing, and then hire someone else to do the rest.
- jeff76 8y agoThis answer makes sense. I suppose my question is more along the lines of, if someone is specializing in deep learning in a PhD program then shouldn’t they at the very least be able to implement models and also know optimization tricks? In other words shouldn’t they be able to develop enough skills to go deep in one area but also know enough to be dangerous in the other three domains?
- agentofoblivion 8y agoI think I agree with you with the caveat that it would depend on what they're researching. If they're researching new model architectures, I don't think it makes sense for them to try to implement the algorithms from scratch in C++/CUDA to do distributed GPU training--why not just use TensorFlow? But if you're researching distributed tensor computation, then that's your bread and butter.