A late reply because I was hoping to dive into this a bit deeper before replying. But due to lack of time, a high-level feedback must suffice: This looks awesome!
I completely agree with your observation that there is a gap between developing prototypes e.g. in Python and bringing them into production -- not only in deep learning, but data science in general. And I also think that Nim's feature set would be perfect to fill this gap. A quick question on using statically-typed tensors: I assume that this implies that the topolgy of a network cannot be dynamic at all? I'm wondering if there are good work-arounds to situations where dynamic network topologies are required, for instance when a model wants to choose its number of hidden layer nodes iteratively, picking the best model variant. Are dynamically typed tensors an option or would that defeat the design / performance?
