From 86174cb8cddc4b3500895bead1c420126e1209d1 Mon Sep 17 00:00:00 2001 From: Ikko Eltociear Ashimine Date: Fri, 9 Jun 2023 01:01:40 +0900 Subject: [PATCH] Fix typo in EmbeddingsTF.ipynb occurences -> occurrences --- lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb b/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb index ffa4e533..e11b248b 100644 --- a/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb +++ b/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb @@ -432,7 +432,7 @@ "\n", "**FastText** tries to overcome the second limitation, and builds on Word2Vec by learning vector representations for each word and the charachter n-grams found within each word. The values of the representations are then averaged into one vector at each training step. While this adds a lot of additional computation to pretraining, it enables word embeddings to encode sub-word information.\n", "\n", - "Another method, **GloVe**, uses a different approach to word embeddings, based on the factorization of the word-context matrix. First, it builds a large matrix that counts the number of word occurences in different contexts, and then it tries to represent this matrix in lower dimensions in a way that minimizes reconstruction loss.\n", + "Another method, **GloVe**, uses a different approach to word embeddings, based on the factorization of the word-context matrix. First, it builds a large matrix that counts the number of word occurrences in different contexts, and then it tries to represent this matrix in lower dimensions in a way that minimizes reconstruction loss.\n", "\n", "The gensim library supports those word embeddings, and you can experiment with them by changing the model loading code above." ]