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Autograd
Efficiently computes derivatives of numpy code
...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. For more information, check out the tutorial and the examples directory. We can continue to differentiate as many times as we like, and use numpy's vectorization of scalar-valued functions across many different input values. -
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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 v.4: https://pragprog.com/book/tpantlr2/the-definitive-antlr-4-reference and help here https://groups.google.com/forum/#!topic/antlr-discussion
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