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doc: Fix minor mistakes

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mpuels 2017-12-14 20:55:59 +01:00 committed by GitHub
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website/usage/_training/_tips.jade
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@ -4,7 +4,7 @@ p
| There are lots of conflicting "recipes" for training deep neural
| networks at the moment. The cutting-edge models take a very long time to
| train, so most researchers can't run enough experiments to figure out
| what's #[em really] going on. For what it's worth, here's a recipe seems
| what's #[em really] going on. For what it's worth, here's a recipe that seems
| to work well on a lot of NLP problems:
+list("numbers")
@ -113,7 +113,7 @@ p
+h(3, "tips-param-avg") Parameter averaging
p
| The last part of our optimisation recipe is #[strong parameter averaging],
| The last part of our optimization recipe is #[strong parameter averaging],
| an old trick introduced by
| #[+a("https://cseweb.ucsd.edu/~yfreund/papers/LargeMarginsUsingPerceptron.pdf") Freund and Schapire (1999)],
| popularised in the NLP community by
@ -126,7 +126,7 @@ p
p
| The trick is to store the moving average of the weights during training.
| We don't optimise this average – we just track it. Then when we want to
| We don't optimize this average – we just track it. Then when we want to
| actually use the model, we use the averages, not the most recent value.
| In spaCy (and #[+a(gh("thinc")) Thinc]) this is done by using a
| context manager, #[+api("language#use_params") #[code use_params]], to