Towards A Visual Programming Tool to Create Deep Learning Models

Tommaso Calò, Luigi De Russis
3/22/2023
cs.HCcs.AIcs.LG

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Abstract

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.

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Cite this paper

@article{cal2023towards,
  title  = {Towards A Visual Programming Tool to Create Deep Learning Models},
  author = {Tommaso Calò and Luigi De Russis},
  year   = {2023},
  eprint = {2303.12821},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.12821v1}
}

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