I've got a problem, which I thought could be solved by using a neural network:
I've got a binary file and a tool that converts that file into a readable html file (probably a text file as well). How the tool is converting that file is a secret and it is too complex to reverse engineer (>100000 lines of assembler for this function) and the binary files are too big and complex to interpret for a human in a feasible time. My idea was to teach a neural network with those binary files and the known output to train the network, how to convert these files. The binary files differ extremely in size(from a few kb up to serval mb).
I've got some basic knowledge of neural networks but I'm not sure where to start to search for the right approach. A friend of mine (with a bit more basic knowledge) said this is definitely doable and suggested a seq2seq model in tensorflow, but after some research I'm not sure if this is the right one for my problem.