Wikipedia defines word embedding as the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.
Word embeddings are a way to transform words in text to numerical vectors so that they can be analyzed by standard machine learning algorithms that require vectors as numerical input.
You have already learned about one type of word embedding called one-hot encoding, in Chapter 1, Neural Networks Foundations. One-hot encoding is the most basic embedding approach. To recap, one-hot encoding represents a word in the text by a vector of the size of the vocabulary, where only the entry corresponding to the word is a one and all the other entries are zero.
A major problem with one-hot encoding is that there is no way to represent the similarity between words...
