4 ms·
Doesn't transfer learning level the playing field a bit? We already have excellent, off-the-shelf models for NLP, computer vision, ODT, etc... that just need fi
by throwaway3699 5y ago
Doesn't transfer learning level the playing field a bit? We already have excellent, off-the-shelf models for NLP, computer vision, ODT, etc... that just need fine-tuning for a particular business or domain problem. I think the 'huge training datasets' requirement is lessening every day.
If you're doing something novel, then sure. I can see that.
- otabdeveloper4 5y agoPredicting time series isn't "novel". It's the oldest data science problem in the world. And ML can't solve it yet.
- throwaway3699 5y agoThen go and use the right tool for the right job. I'm not suggesting neural networks are a panacea. I've worked with time-series problems recently and not used NN. NN not being good at one class of problems does not invalidate their usefulness for other classes, like your GP comment is suggesting.
- otabdeveloper4 5y ago> ...like your GP comment is suggesting Really? What I literally wrote is "neural networks have extremely limited applicability". > I'm not suggesting neural networks are a panacea. That's good. Because ML practitioners are, even if they phrase it differently. (The idea is that since neural networks can fit curves then any problem can be solved by a neural network given enough layers and feature engineering grease.)