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Alec Helbling
2023-07-19 16:47:52 -04:00
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[![GitHub tag](https://img.shields.io/github/v/release/helblazer811/ManimMachineLearning)](https://img.shields.io/github/v/release/helblazer811/ManimMachineLearning) [![GitHub tag](https://img.shields.io/github/v/release/helblazer811/ManimMachineLearning)](https://img.shields.io/github/v/release/helblazer811/ManimMachineLearning)
[![Downloads](https://static.pepy.tech/badge/manim-ml)](https://pepy.tech/project/manim-ml) [![Downloads](https://static.pepy.tech/badge/manim-ml)](https://pepy.tech/project/manim-ml)
ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the [Manim Community Library](https://www.manim.community/). We want this project to be a compilation of primitive visualizations that can be easily combined to create videos about complex machine learning concepts. Additionally, we want to provide a set of abstractions which allow users to focus on explanations instead of software engineering. ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the [Manim Community Library](https://www.manim.community/). Please check out [our paper](https://arxiv.org/abs/2306.17108). We want this project to be a compilation of primitive visualizations that can be easily combined to create videos about complex machine learning concepts. Additionally, we want to provide a set of abstractions which allow users to focus on explanations instead of software engineering.
*A sneak peak ...* *A sneak peak ...*
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ManimML supports visualizations of Convolutional Neural Networks. You can specify the number of feature maps, feature map size, and filter size as follows `Convolutional2DLayer(num_feature_maps, feature_map_size, filter_size)`. There are a number of other style parameters that we can change as well(documentation coming soon). ManimML supports visualizations of Convolutional Neural Networks. You can specify the number of feature maps, feature map size, and filter size as follows `Convolutional2DLayer(num_feature_maps, feature_map_size, filter_size)`. There are a number of other style parameters that we can change as well(documentation coming soon).
Here is a multi-layer convolutional neural network. If you are unfamiliar with convolutional networks [this overview](https://cs231n.github.io/convolutional-networks/) is a great resource. You need to be careful that the feature map sizes and filter dimensions of adjacent layers match up. Here is a multi-layer convolutional neural network. If you are unfamiliar with convolutional networks [this overview](https://cs231n.github.io/convolutional-networks/) is a great resource. Additionally, [CNN Explainer](https://poloclub.github.io/cnn-explainer/) is a great interactive tool for understanding CNNs, all in the browser.
When specifying CNNs it is important for the feature map sizes and filter dimensions of adjacent layers match up.
```python ```python
from manim_ml.neural_network import NeuralNetwork, FeedForwardLayer, Convolutional2DLayer from manim_ml.neural_network import NeuralNetwork, FeedForwardLayer, Convolutional2DLayer
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If you found ManimML useful please cite it below! If you found ManimML useful please cite it below!
``` ```
@software{alec_helbling_2023_7760911, @misc{helbling2023manimml,
author = {Alec Helbling}, title={ManimML: Communicating Machine Learning Architectures with Animation},
title = {{ManimML: A Python Animation Engine for Machine author={Alec Helbling and Duen Horng and Chau},
Learning Architectures}}, year={2023},
month = mar, eprint={2306.17108},
year = 2023, archivePrefix={arXiv},
publisher = {Zenodo}, primaryClass={cs.LG}
version = {v0.0.20},
doi = {10.5281/zenodo.7760911},
url = {https://doi.org/10.5281/zenodo.7760911}
} }
``` ```