  Deep Learning (DL) developers come from different backgrounds, e.g.,
medicine, genomics, finance, and computer science. To create a DL model, they
must learn and use high-level programming languages (e.g., Python), thus
needing to handle related setups and solve programming errors. This paper
presents DeepBlocks, a visual programming tool that allows DL developers to
design, train, and evaluate models without relying on specific programming
languages. DeepBlocks works by building on the typical model structure: a
sequence of learnable functions whose arrangement defines the specific
characteristics of the model. We derived DeepBlocks' design goals from a
5-participants formative interview, and we validated the first implementation
of the tool through a typical use case. Results are promising and show that
developers could visually design complex DL architectures.
