The Difference Between Deep Learning & Machine Learning

A popular notion about machine learning models is the interpretability -- statistical models like logistic regression yield interpretable models. Historically, financial sector which relies heavily on interpretability uses machine learning models because of its ability to offer an audit trail. On the other hand, neural networks are dubbed as the black boxes since there is no real understanding of how the output was achieved. In other words, one may not be able to ascertain how the exact model works inside and out, but know the learning algorithm that created it. However, according to Zachary Chase Lipton, a Ph.D student at UCSD, machine learning algorithms, for example decision trees, often championed for their interpretability, can also be similarly opaque.