Autograd can automatically differentiate native Python and Numpy code. It can
handle a large subset of Python's features, including loops, ifs, recursion and
closures, and it can even take derivatives of derivatives of derivatives. It
supports reverse-mode differentiation (a.k.a. backpropagation), which means it
can efficiently take gradients of scalar-valued functions with respect to
array-valued arguments, as well as forward-mode differentiation, and the two
can be composed arbitrarily. The main intended application of Autograd is
gradient-based optimization.