The main goal of Declarative Learning Based Programming workshop is to investigate the issues that arise when designing and using programming languages that support learning from data and knowledge. Declarative learning based programming aims at new programming models and abstractions that facilitate the design and development of intelligent real world applications that use machine learning, deep learning and reasoning.The challenges of such a paradigm include interaction with messy, naturally occurring data; specifying the requirements of the application at a high abstraction level; dealing with uncertainty in various layers of the application program; supporting flexible relational feature engineering and learning rich data representations; using representations that support flexible reasoning, structured and deep learning; supporting model chaining and composition; integrating a range of learning and inference algorithms; and, finally, addressing the above mentioned issues in one unified programming environment.Conventional programming languages offer no help to application programmers that attempt to design and develop applications that make use of real world data, and reason about it in a way that involves learning interdependent concepts from data, incorporating and composing existing models, and reasoning about existing and trained models and their parameterization. The research community has tried to address these problems from multiple perspectives, most notably various approaches based on Probabilistic programming, Logical Programming and the integrated paradigms. The goal of this workshop is to present and discuss the current related research and the way various challenges have been addressed.We aim at motivating the need for further research toward a unified framework in this area based on the key existing paradigms: probabilistic programming, logic programming, probabilistic logical programming, first-order query languages and database management systems and deductive databases, statistical relational learning and related languages, declarative deep learning frameworks and connect these to the ideas of declarative learning based programming. We aim to discuss and investigate the required type of languages and representations that facilitate modeling complex learning models, deep architectures, and provide the ability to combine, chain and perform flexible inference by exploiting domain knowledge.Though the theme of this workshop remains generic, we aim at emphasizing on ideas and opinions regarding conceptual representations of deep learning architectures that connect various computational units to the semantics of declarative data and knowledge representations. We are interested in the abstractions that in contrast to the existing ones (for example, tensor flow), are away from the underlying computational units and are towards declarative domain representations while are expressive enough to exploit the deep configurations and computations.