Demystifying Deep Learning with Innovative Explainability Techniques

As all practitioners know, deep learning models are like a black box. You have some inputs, pass them through a black box, and get the output. Many people have researched ways in which to see into that black box since algorithm transparency is required both for development and regulatory purposes. However, most solutions are purely scientific or code-based and hard to implement and visualize.