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Much of machine learning boils down to constructing a loss function and optimizing it, often using gradients. Reverse-mode differentation (sometimes called "backpropagation") is a general and computationally efficient way to compute these gradients. I'll explain reverse-mode differentiation and show how we've implemented it for Python/Numpy in our automatic differentation package . I'll finish with some demos showing how easy it is to implement several machine learning models once you have automatic differentiation in your toolbox.