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 # AutomaticDifferentiation
 Templated c++ Forward Automatic differentiation.
+
+There are two versions :
+- A scalar one,
+- a vectorized one.
+
+The class is a simple one, no expression templates are used. The class is however a template, meaning that any base numeric type can be used with it. It has successfully tested with boost::multiprecision::mpfr.
+
+## Scalar version
+The scalar one allows very easily to produce higher order derivatives.
+
+## Vector version
+The vectorized one is harder to make work with higher order derivatives, but allows the simultaneous computation of the full gradient, in a single function call, making it more efficient than backward automatic differentiation.
+It currently depends on Eigen for the vectorized part.