ManimML

Why we included this project

ManimML is for people who need to show how a neural network actually behaves during a forward pass, not just draw a static diagram of it. Built on the Manim Community library, it wraps animation in a PyTorch-like API: you describe the layers in familiar syntax and get animated visuals of forward propagation, convolutions, max pooling, dropout, and activation functions. The pieces are reusable, so you compose them and tweak parameters to focus on whatever part of the explanation matters most, instead of hand-coding every frame. It installs with pip and runs from ordinary Python scripts, which makes it a practical fit for teachers, course creators, and technical presenters producing 3Blue1Brown-style clips for lectures, documentation, or talks. A linked paper documents the design, which helps if you want to know how the abstractions work before adopting it.