I agree with Phil .. I’ve actually fallen into a pattern where i use a Mac Pro for work, but use the iPad Pro as my personal computer .. for tasks such as reading, surfing, reserving tickets, listening to music and writing
Applications that employ computer vision, machine learning or even deep learning, especially if they are trying to run models on mobile devices have to write their codebases on an efficient high-performance language otherwise, their apps will be just too computationally expensive (too slow).
At my company, three quarters of our codebase is written in C++ because performance is very critical for us, in addition portability! i.e we can compile to run the same code on different platforms without added layers of translation.
I would be interested to know how he trained a fear detector.
There are a lot of companies that develop software that track facial expressions.
It is conceivable to use Affectiva's SDKs to automatically annotate data for facial expressions and then use that data to develop models that correlate facial expressions or facial expressions of emotions into things like performance prediction ...
Affectiva offers SDKs for facial expression and emotion analysis from images that work in realtime and offline without having to send images to the cloud.