In this work we present and briefly review the Deep Unfolding, a recent machine learning paradigm that found natural application in scientific computing thanks to its promising strength in generating highly interpretable deep neural networks apt to be employed even on limited-resourced embedded systems. We describe this technique within a bilevel optimization framework and provide several exemplar applications, mainly focusing on Nonnegative Matrix Factorization.