  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.
