The past decade has witnessed an exponential increase in data and the rise of deep learning systems to handle it. These systems primarily analyze, learn, and interpret the data by excelling in exploiting patterns. This leads us to the question: can we use these patterns to efficiently store the data as well? Essentially, this would mean moving from the paradigm of data compression towards model compression. This talk introduces and investigates a variety of methods in the realm of model compression, with the eventual goal of combining compression and understanding.