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Neural network architectures are most often conceptually designed anddescribed in visual terms, but are implemented by writing error-prone code.PrototypeML is a machine learning development environment that bridges thedichotomy between the design and development processes: it provides a highlyintuitive visual neural network design interface that supports (yet abstracts)the full capabilities of the PyTorch deep learning framework, reduces modeldesign and development time, makes debugging easier, and automates manyframework and code writing idiosyncrasies. In this paper, we detail the deeplearning development deficiencies that drove the implementation of PrototypeML,and propose a hybrid approach to resolve these issues without limiting networkexpressiveness or reducing code quality. We demonstrate the real-world benefitsof a visual approach to neural network design for research, industry andteaching. Available at https://PrototypeML.com

PrototypeML: A Neural Network Integrated Design and Development Environment