Data Science, Machine Learning, NLP, Word Embeddings, AI.
above, the result would look like this: Word vectors using one-hot encoding. Each word is represented by a vector that is mostly zeroes, except there is a single “1” in the position dictated by that word’s index in the vocabulary. Note: it’s not that “black”, “cat”, and “duvet” have the same