48c3a3c8f4
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 uses reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments. The main intended application is gradient-based optimization.
8 lines
470 B
Text
8 lines
470 B
Text
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 uses reverse-mode differentiation
|
|
(a.k.a. backpropagation), which means it can efficiently take
|
|
gradients of scalar-valued functions with respect to array-valued
|
|
arguments. The main intended application is gradient-based
|
|
optimization.
|