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Deep Learning (DL) developers come from different backgrounds, e.g.,medicine, genomics, finance, and computer science. To create a DL model, theymust learn and use high-level programming languages (e.g., Python), thusneeding to handle related setups and solve programming errors. This paperpresents DeepBlocks, a visual programming tool that allows DL developers todesign, train, and evaluate models without relying on specific programminglanguages. DeepBlocks works by building on the typical model structure: asequence of learnable functions whose arrangement defines the specificcharacteristics of the model. We derived DeepBlocks' design goals from a5-participants formative interview, and we validated the first implementationof the tool through a typical use case. Results are promising and show thatdevelopers could visually design complex DL architectures.

Towards A Visual Programming Tool to Create Deep Learning Models