ManimML: Communicating Machine Learning Architectures with Animation

Alec Helbling; Duen Horng Chau

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arXiv · 2306.17108

ManimML: Communicating Machine Learning Architectures with Animation

Alec Helbling·Duen Horng Chau

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Abstract

There has been an explosion in interest in machine learning (ML) in recent years due to its applications to science and engineering. However, as ML techniques have advanced, tools for explaining and visualizing novel ML algorithms have lagged behind. Animation has been shown to be a powerful tool for making engaging visualizations of systems that dynamically change over time, which makes it well suited to the task of communicating ML algorithms. However, the current approach to animating ML algorithms is to handcraft applications that highlight specific algorithms or use complex generalized animation software. We developed ManimML, an open-source Python library for easily generating animations of ML algorithms directly from code. We sought to leverage ML practitioners' preexisting knowledge of programming rather than requiring them to learn complex animation software. ManimML has a familiar syntax for specifying neural networks that mimics popular deep learning frameworks like Pytorch. A user can take a preexisting neural network architecture and easily write a specification for an animation in ManimML, which will then automatically compose animations for different components of the system into a final animation of the entire neural network. ManimML is open source and available at https://github.com/helblazer811/ManimML.

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BibTeX RIS

Alec Helbling, Duen Horng Chau. 2023-11-14. ManimML: Communicating Machine Learning Architectures with Animation. https://arxiv.org/abs/2306.17108

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