> > It depends. The frontier between data and code is not as clear as it may > seem. An example: the weights of a trained neural network can be seen as > data (a bunch of numbers), but also as code for a special-purpose > processor defined by the neural network. > > Starting from that example, consider that the weights of a neural > network are not fundamentally different from fit parameters in other > scientific models. For example the positions of the atoms in a protein > structure. Using the same analogy as for the neural network, these > positions are the code for a special-purpose processor that computes > estimations for the Bragg reflections that are measured in protein > crystallography. >
Sorry for bothering with a completely unrelated topic. I'm curious do you train neural network with packages in Guix? Or did you packaged related libraries yourself? I would love to let guix handle all machine learning libraries for me, but many of them, especially those nn libraries, require GPU to accelerate the speed. But, AFAIK, currently there are no GPU compute stack can meet GNU's standard. Unable to put it upstream somehow makes me lazy to package them. Thanks.
