From c7047906d4d29a1d38cff20d7588ae1836a271de Mon Sep 17 00:00:00 2001 From: Jen Looper Date: Mon, 23 May 2022 14:02:10 -0400 Subject: [PATCH] adding quiz links --- lessons/5-NLP/15-LanguageModeling/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lessons/5-NLP/15-LanguageModeling/README.md b/lessons/5-NLP/15-LanguageModeling/README.md index 961c75b7..29023275 100644 --- a/lessons/5-NLP/15-LanguageModeling/README.md +++ b/lessons/5-NLP/15-LanguageModeling/README.md @@ -3,7 +3,7 @@ Semantic embeddings, such as Word2Vec and GloVe, are in fact a first step towards **language modeling** - creating models that somehow *understand* (or *represent*) the nature of the language. -## [Pre-lecture quiz](tbd) +## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/115) The main idea behind language modeling is training them on unlabeled datasets in an unsupervised manner. This is important because we have huge amounts of unlabeled text available, while the amount of labeled text would always be limited by the amount of effort we can spend on labeling. Most often, we can build language models that can **predict missing words** in the text, because it is easy to mask out a random word in text and use it as a training sample. @@ -25,7 +25,7 @@ TBD TBD -## [Post-lecture quiz](tbd) +## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/215) ## Review & Self Study