non-parallelisable logical depth
How Well Does Deep Learning Models Perform In Theory
Behind every successful scientific implementation, there is a theory that supports the results or allows one to anticipate the consequences. In the case of machine learning, however, the situation is a bit counterintuitive. Though the number of implementations of ML is spiking every day, one still cannot pinpoint the reason why a particular model is making some predictions. Machine learning models are called black-box models for a reason! Why does a certain model work?