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Autograd
Efficiently computes derivatives of numpy code
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.... -
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This is trying to create code analyzer for Harbour language code and add it later in HbIDE and use it later in for NetBeans plugin and use it later in other IDEs. I use GIT here. Harbour project https://harbour.github.io/ Docs about ANTLR: https://tomassetti.me/antlr-mega-tutorial http://www.xfront.com/ANTLR/ about v.3: http://www.r-5.org/files/books/computers/compilers/writing/Terence_Parr-The_Definitive_ANTLR_Reference-EN.pdf about...
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