Operations on word vectors

Welcome to your first assignment of this week!

Because word embeddings are very computationally expensive to train, most ML practitioners will load a pre-trained set of embeddings.

After this assignment you will be able to:

• Load pre-trained word vectors, and measure similarity using cosine similarity
• Use word embeddings to solve word analogy problems such as Man is to Woman as King is to ______.
• Modify word embeddings to reduce their gender bias

Updates

If you were working on the notebook before this update...

• The current notebook is version "2a".
• You can find your original work saved in the notebook with the previous version name ("v2")
• To view the file directory, go to the menu "File->Open", and this will open a new tab that shows the file directory.

List of updates

• cosine_similarity
  • Additional hints.
• complete_analogy
  • Replaces the list of input words with a set, and sets it outside the for loop (to follow best practices in coding).
• Spelling, grammar and wording corrections.

Let's get started! Run the following cell to load the packages you will need.

Load the word vectors

• For this assignment, we will use 50-dimensional GloVe vectors to represent words.
• Run the following cell to load the word_to_vec_map.