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Building a career around a framework is never a good idea. If you know your shit, it shouldn't matter what framework you're using. Theano and TF, for example,
by wjn0 10y ago
Building a career around a framework is never a good idea. If you know your shit, it shouldn't matter what framework you're using.
Theano and TF, for example, both make similar abstractions: graphs and numerical functions on top of the same matrix library, even. I would suspect someone could move between the two fairly easily. The problem is that a programmer can use TF/Theano/etc.'s built-in gradient descent functions pulled from a tutorial with their data subbed in _instead_ of learning the details of backpropogation, end up with decent results, and claim to have a basic understanding of ML - when really, they've managed to avoid it almost completely.
- kafkaesq 10y agoIf you know your shit, it shouldn't matter what framework you're using. And yet, what is the incessant drumbeat of most job ads, these days - even in data science? That's right: "N years in framework X"
- aianus 10y ago> That's right: "N years in framework X" I don't know where this meme comes from. Six out of the seven jobs/internships I've had (in SF, Seattle, and Toronto) didn't care about any specific framework or language whatsoever and still don't.
- wjn0 10y agoI don't follow job postings for ML, but I will say this - there is inherent value in being familiar with certain frameworks when hiring for non-entry-level positions. This is particularly true for ML, where it can be very useful to know the optimizations that the libraries do/don't make behind the scenes, etc. Should most ML job postings require n years of experience in some framework? Maybe not - but I can see why a company might see value in it.