One of the very interesting questions that really got me thinking yesterday (they all did to an important degree) was from @Jeande_d regarding how to balance between learning foundational/transferable skills vs focusing on specific tools.
https://twitter.com/Jeande_d/status/1355521414099587072
@Jeande_d My reasoning was that one should try hard not to learn too much of a tool, because any tool will eventually disappear. But tools are crucial to be productive, so one should still learn enough to really take advantage of the unique features of that tool.
@Jeande_d One way I think you can try to hit that sweet spot is practice some sort of dropout regularization on your common tool set.
In every new project, substitute one of your usual tools for some convenient alternative. It will make you a bit less productive, to be sure...
In every new project, substitute one of your usual tools for some convenient alternative. It will make you a bit less productive, to be sure...
@Jeande_d ... and sometimes you really cannot afford that loss in productivity.
But try to force yourself to do it as often as possible. If I were to find out that I cannot really stop using, say, React, then I would be very worried about my future self.
But try to force yourself to do it as often as possible. If I were to find out that I cannot really stop using, say, React, then I would be very worried about my future self.
@Jeande_d Right now, that tool for me is probably scikit-learn. There are some things that I cannot see myself doing without it, and that worries me. So next time I have to do some shallow machine learning, I want to use an alternative.
Any suggestions?
Any suggestions?
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