In summary, skip-gram trains a probabilistic classifier that, given a test target word w and its context window of L words c 1:L , assigns a probability based on how similar this context window is to the target word. The probability is based on applying the logistic (sigmoid) function to the dot product of the embeddings of the target word with each context word. To compute this probability, we just need embeddings for each target word and context word in the vocabulary. Figure 6 .13 The embeddings learned by the skipgram model. The algorithm stores two embeddings for each word, the target embedding (sometimes called the input embedding) and the context embedding (sometimes called the output embedding). The parameter θ that the algorithm learns is thus a matrix of 2|V | vectors, each of dimension d, formed by concatenating two matrices, the target embeddings W and the context+noise embeddings C. Fig. 6 .13 shows the intuition of the parameters we'll need. Skip-gram actually stores two embeddings for each word, one for the word as a target, and one for the word considered as context. Thus the parameters we need to learn are two matrices W and C, each containing an embedding for every one of the |V | words in the vocabulary V . 6 Let's now turn to learning these embeddings (which is the real goal of training this classifier in the first place).