Sequence Models | Attention Model | Word Embeddings | More about Data Science
1. What are word representations?
- One-Hot encodings
- Featurized representaion: word embeddings
- Word embedding is one of the most popular word representation.
- It is capable of capturing context of a word in a document, semantic and syntactic similarity, relation with other words, etc.
2. Summary of word embeddings
| Word Embeddings | |
|---|---|
| Transfer Learning | yes |
| Training Set | small |
| Entity recognition, text summarization, co-reference resolution, parsing | Good |
| Language modeling and machine translation | Bad |
| Visulization | t-SNE |
3. How does word embedding algorithms work?
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How to use word embeddings to solve a Word Analogy problem?
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How to load pre-trained word vectors, and Measure Similarity using cosine similarity
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How to modify word embeddings to reduce their gender bias
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Try the exercise on Coursera Coursera notebook
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Take a look at my notebook
4. How to create an embedding layer and build a Kears LSTM model for sentiment classification?
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Try the Emotify exercise on Coursera Coursera notebook
(Input a sentence and find the most appropriate emoji) -
Take a look at my notebook