Model-based deep learning

This monograph provides a tutorial style presentation of model-based deep learning methodologies. These are families of algorithms that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches. Such model-based deep learning methods exploit both partial domain knowledge, via mathematical structures designed for specific problems, as well as learning from limited data. The monograph includes running signal processing examples, in super-resolution, tracking of dynamic systems, and array processing. It is shown how they are expressed using the provided characterization and specialized in each of the detailed methodologies. The aim is to facilitate the design and study of future systems at the intersection of signal processing and machine learning that incorporate the advantages of both domains. The source code of the numerical examples are available and reproducible as Python notebooks.