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In the last video, you saw how the Skip-Gram model allows you to construct a supervised learning task. So we map from context to target and how that allows you to learn a useful word embedding. But the downside of that was the Softmax objective was slow to compute. In this video, youll see a modified learning problem called negative sampling that allows you to do something similar to the Skip-Gram model you saw just now, but with a much more efficient learning algorithm. Lets see how you can do this. Most of the ideas presented in this video are due to Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeff Dean. So what were going to do in this algorithm is create a new supervised learning problem. And the problem is, given a pair of words like orange and juice, were going to predict, is this a context-target pair? So in this example, orange juice was a positive example. And how about orange and king? Well, thats a negative example, so Im going to write 0 for the target.
