Yes - basic PPT to slick presentation would be really useful, I like writing content but the layout formatting is boring as far as I'm concerned

I think pytorch is a good level for understanding what's going on building networks but not knee deep in mathematics and fundamentals.

Even then there are quite a lot of prepackaged networks bundled into pytorch; from what I remember you can just instantiate AlexNet with one line.

The impact of training time is not linear so neither are the benefits of speeding up. For example, going from 1hr to 5 minutes would be useful for experimentation/early development phases. However once I am training a model for Production then 12 hours overnight is fine. I have other things to do to fill the time. I think what is useful for faster training is to be able to see that the model is converging.

Inference time is important up to a point but performance engineering is about steady optimisation over the whole system. You can reach a floor on one part like inference and still have work in network or cpu-bound stages.