The objective of skip-gram model is to find representations of words that are useful for predicting the 'contexts' — surrounding words.
Given a sequence of training words \(w_1, w_2, \ldots, w_T\),
$$ \frac{1}{T} \sum_{t=1}^{T} \sum_{-c \ge j \ge c, j \neq 0} \log p(w_{t+j} | w_t) $$
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