### Automatic differentiation in  C++ ###

Day 8: Forward mode implementation part 1

I've figured out how to implement the forward mode for the simple cases: 
Case 1: input x is a vector and output y is a real number.
The second case is just a generalization of the output (y is also a vector).

I had some cool small examples of the manual execution and values of the gradients trace, but it got deleted somehow 🥲 I'll do it again and share it the next day!

In forward mode, it seems that each forward pass can compute the gradient of the output with respect to only one input at a time. So if the input is a vector, would that be considered as 1 input or would each value in that vector be considered as 1 input (so x has n inputs basically)?

Quick update, I've decided to make the implementation consider the input vector as multiple inputs (x_1, x_2..., x_n), seems to be what's FM is intended to be implemented as. I'll finish it today hoepfully and share the push the code to git soon!

Great, can't wait to seen it!
4370 $degen