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hi Im Rachel from Raza what our word embeddings where should you use them and what are some common errors you might run into well cover all of that today in NLP for developers so a word embedding or a word vector is a numeric vector input that represents a word so instead of the characters for the word the as input to your system you would use a here we have length 500 numeric vector to represent the and every word in your vocabulary would have a unique vector associated with it they also can approximate meaning so how does that work well its based on the distributional hypothesis and semantics which is this idea that words that are more similar to each other are going to be used together more often so cat and dog are going to be used more with the word veterinarian that they will with the word teakettle and the idea is that you can use that information to deduce that cat dog and veterinarian are similar to each other in meaning than cat dog and tea kettle lets work through a quick