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The hard parts of these frameworks isn't the framework itself. The hard part is understanding what's going on; the same properties common to all frameworks. CN
by gcr 10y ago
The hard parts of these frameworks isn't the framework itself. The hard part is understanding what's going on; the same properties common to all frameworks.
CNNs are hard to understand. Understanding why gradient descent improves the model is very hard to understand. And understanding the right architecture to solve a problem takes a significant chunk of your research career unless you're already an expert.
But languages and frameworks are simple. If you know Caffe, Torch will be easy to understand. If you know Torch, Tensorflow will be easy to understand. If you know Tensorflow, the others will be easy to understand. It's quite possible to get up to speed with a different framework in a few weeks.
- guelo 10y agoIn the corporate world even a few weeks of ramp-up time can create lock-in and network effects. Most programming languages can be learned in a few weeks by an experienced programmer, but there are still huge network effects. Part of it is that a lot of times you just don't have a few weeks to learn. But the other part of it is that the more popular it gets the library and tooling ecosystem also starts growing exponentially, so then you have to add the learning time of the tooling and library to the switching cost.
- ringwalt 10y agoI think the network effects have more to do with clueless recruiters trying to tick off a list of keywords for a position, than the actual cost to a company of an engineer ramping up on a new framework for a few weeks.