Automatic Differentiation
Automatic Differentiation (AD) is a set of techniques used to numerically evaluate the derivative of a function. It is a computational method that allows for the accurate and efficient evaluation of derivatives, even for complex functions with many variables. AD works by decomposing a function into a sequence of elementary operations, and then applying the chain rule of differentiation to compute the derivative of the function. There are two main approaches to AD: forward mode and reverse mode. In forward mode, the derivative is computed by propagating the derivative of each elementary operation through the computation graph. In reverse mode, the derivative is computed by propagating the derivative of the output of the function backwards through the computation graph. AD has many applications in machine learning, optimization, and scientific computing.
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