Recent years have witnessed a dramatically growing interest in machine learning (ML) methods. These data-driven trainable structures have demonstrated an unprecedented empirical success in various applications, including computer vision and speech processing. The benefits of ML-driven techniques over traditional model-based approaches are twofold: First, ML methods are independent of the underlying stochastic model, and thus can operate efficiently in scenarios where this model is unknown, or its parameters cannot be accurately estimated; Second, when the underlying model is extremely complex, ML algorithms have demonstrated the ability to extract and disentangle the meaningful semantic information from the observed data. Nonetheless, not every problem can and should be solved using deep neural networks (DNNs). In fact, in scenarios for which model-based algorithms exist and are computationally feasible, which is the case in various signal processing and communications setups, these analytical methods are typically preferable over ML schemes due to their theoretical performance guarantees and possible proven optimality. In my work I study how DNNs can be combined with classic methods into hybrid model-based/data-driven algorithms which leverage the model-agnostic nature of deep learning while preserving the interpretability and suitability of classic methods. My goal is therefore to allow model-based algorithms to be applied in scenarios for which, due to either a complex underlying statistical model or missing knowledge of it, these methods cannot be applied directly. This is achieved by exploring the continuous spectrum between data-centric deep learning and knowledge-centric model-based algorithms.