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Fair enough. That's a useful comparison to know about. But I'm wondering how you get around that with the neural net. In the post, you said there are only a fe
by rspeer 10y ago
Fair enough. That's a useful comparison to know about.
But I'm wondering how you get around that with the neural net. In the post, you said there are only a few hundred labeled examples, right? How can a neural net with hundreds of parameters set those parameters to anything reasonable, and not overfit, when there are about as many parameters as examples?
- bowlesbe 10y agoGreat question and I share your intuition but I think its all properly regularizing your model. I guess for neural networks, Dropout works really darn well as a regularization strategy. I could have tried to see whether performance dropped significantly without dropout.