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.

翻译：深度学习（DL）开发者背景各异，涵盖医学、基因组学、金融及计算机科学等领域。为构建深度学习模型，他们需学习并使用高级编程语言（如Python），从而必须处理相关环境配置并解决编程错误。本文提出DeepBlocks——一种无需依赖特定编程语言即可设计、训练及评估模型的视觉编程工具。该工具基于典型模型结构构建：一系列可学习函数的排列定义了模型的具体特征。我们通过一项面向5名参与者的形成性访谈提炼出DeepBlocks的设计目标，并通过典型用例验证了该工具的首个实现版本。结果表明，开发者能够以可视化方式设计复杂的深度学习架构，这一成果令人鼓舞。