# ManimML: Visualizing Machine Learning Concepts with Manim Animations

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ManimML is an open-source project that offers powerful animations and visualizations for common machine learning concepts, built upon the Manim Community Library. It aims to provide a compilation of primitive visualizations that can be easily combined to explain complex ML ideas, allowing users to focus on explanations rather than software engineering. The library simplifies the creation of engaging educational content for neural networks, convolutional layers, and more.

GitHub: https://github.com/helblazer811/ManimML
OSRepos URL: https://osrepos.com/repo/helblazer811-manimml

## Summary

ManimML is an open-source project that offers powerful animations and visualizations for common machine learning concepts, built upon the Manim Community Library. It aims to provide a compilation of primitive visualizations that can be easily combined to explain complex ML ideas, allowing users to focus on explanations rather than software engineering. The library simplifies the creation of engaging educational content for neural networks, convolutional layers, and more.

## Topics

- 3blue1brown
- machine-learning
- manim
- neural-network
- visualization
- Python
- Animation
- Education

## Repository Information

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## Content

## Introdução
ManimML is a project dedicated to creating stunning animations and visualizations of common machine learning concepts using the Manim Community Library. The goal is to offer a collection of fundamental visualizations that can be readily combined to produce videos explaining intricate machine learning ideas. This project also provides abstractions, enabling users to concentrate on educational content rather than the underlying software engineering. ManimML has even been featured in a [paper](https://arxiv.org/abs/2306.17108){:target="_blank"}, highlighting its contribution to communicating machine learning architectures.

## Instalação
To get started with ManimML, you first need to install the Manim Community edition. Ensure it's not the original 3Blue1Brown Manim version.
You can find detailed installation instructions for Manim [here](https://docs.manim.community/en/stable/installation.html){:target="_blank"}.

Once Manim is installed, you can install ManimML via pip:
bash
pip install manim_ml

Note that some recent features might only be available if you install directly from the source.

## Exemplos
ManimML provides a straightforward way to visualize various machine learning architectures and processes. Here are some examples:

**First Neural Network (Convolutional Neural Network)**
This example demonstrates how to visualize a Convolutional Neural Network and animate its forward pass.
python
from manim import *
from manim_ml.neural_network import Convolutional2DLayer, FeedForwardLayer, NeuralNetwork

config.pixel_height = 700
config.pixel_width = 1900
config.frame_height = 7.0
config.frame_width = 7.0

class BasicScene(ThreeDScene):
    def construct(self):
        nn = NeuralNetwork([
                Convolutional2DLayer(1, 7, 3, filter_spacing=0.32),
                Convolutional2DLayer(3, 5, 3, filter_spacing=0.32),
                Convolutional2DLayer(5, 3, 3, filter_spacing=0.18),
                FeedForwardLayer(3),
                FeedForwardLayer(3),
            ],
            layer_spacing=0.25,
        )
        nn.move_to(ORIGIN)
        self.add(nn)
        forward_pass = nn.make_forward_pass_animation()
        self.play(forward_pass)

To render this, save the code as `example.py` and run:
bash
manim -pql example.py

![Convolutional Neural Network](assets/readme/convolutional_neural_network.gif)

**A Simple Feed Forward Network**
Visualize a basic feed-forward neural network by defining its layers.
python
from manim_ml.neural_network import NeuralNetwork, FeedForwardLayer

nn = NeuralNetwork([
    FeedForwardLayer(num_nodes=3),
    FeedForwardLayer(num_nodes=5),
    FeedForwardLayer(num_nodes=3)
])
self.add(nn)

![Simple Feed Forward Network](assets/readme/a_simple_feed_forward_neural_network.png)

**Animating the Forward Pass**
Automatically animate the data flow through your neural network.
python
from manim_ml.neural_network import NeuralNetwork, FeedForwardLayer

nn = NeuralNetwork([
    FeedForwardLayer(num_nodes=3),
    FeedForwardLayer(num_nodes=5),
    FeedForwardLayer(num_nodes=3)
])
self.add(nn)
forward_pass_animation = nn.make_forward_pass_animation()
self.play(forward_pass_animation)

![Animating Forward Pass](assets/readme/animating_the_forward_pass.gif)

**Convolutional Neural Networks with an Image**
Feed an image into a convolutional neural network and visualize its processing.
python
import numpy as np
from PIL import Image
from manim_ml.neural_network import NeuralNetwork, FeedForwardLayer, Convolutional2DLayer, ImageLayer

image = Image.open("digit.jpeg") # You will need to download an image of a digit.
numpy_image = np.asarray(image)

nn = NeuralNetwork([
        ImageLayer(numpy_image, height=1.5),
        Convolutional2DLayer(1, 7, 3, filter_spacing=0.32),
        Convolutional2DLayer(3, 5, 3, filter_spacing=0.32),
        Convolutional2DLayer(5, 3, 3, filter_spacing=0.18),
        FeedForwardLayer(3),
        FeedForwardLayer(3),
    ],
    layer_spacing=0.25,
)
nn.move_to(ORIGIN)
self.add(nn)
forward_pass = nn.make_forward_pass_animation()

![CNN with Image](assets/readme/convolutional_neural_network_with_an_image.gif)

**Max Pooling**
Visualize the 2D Max Pooling operation, a common technique to reduce feature map sizes.
python
from manim_ml.neural_network import NeuralNetwork, Convolutional2DLayer, MaxPooling2DLayer

nn = NeuralNetwork([
        Convolutional2DLayer(1, 8),
        Convolutional2DLayer(3, 6, 3),
        MaxPooling2DLayer(kernel_size=2),
        Convolutional2DLayer(5, 2, 2),
    ],
    layer_spacing=0.25,
)
nn.move_to(ORIGIN)
self.add(nn)
forward_pass = nn.make_forward_pass_animation()
self.wait(1)
self.play(forward_pass)

![Max Pooling](assets/readme/max_pooling.gif)

**Activation Functions**
Illustrate different activation functions applied to neural network layers.
python
from manim_ml.neural_network import NeuralNetwork, Convolutional2DLayer, FeedForwardLayer

nn = NeuralNetwork([
        Convolutional2DLayer(1, 7, filter_spacing=0.32),
        Convolutional2DLayer(3, 5, 3, filter_spacing=0.32, activation_function="ReLU"),
        FeedForwardLayer(3, activation_function="Sigmoid"),
    ],
    layer_spacing=0.25,
)
self.add(nn)
forward_pass = nn.make_forward_pass_animation()
self.play(forward_pass)

![Activation Functions](assets/readme/activation_functions.gif)

**Neural Network Dropout**
Animate the dropout regularization technique in a neural network.
python
from manim_ml.neural_network import NeuralNetwork, FeedForwardLayer
from manim_ml.neural_network.animations.dropout import make_neural_network_dropout_animation

nn = NeuralNetwork([
        FeedForwardLayer(3),
        FeedForwardLayer(5),
        FeedForwardLayer(3),
        FeedForwardLayer(5),
        FeedForwardLayer(4),
    ],
    layer_spacing=0.4,
)
nn.move_to(ORIGIN)
self.add(nn)
self.play(
    make_neural_network_dropout_animation(
        nn, dropout_rate=0.25, do_forward_pass=True
    )
)
self.wait(1)

![Neural Network Dropout](assets/readme/dropout.gif)

## Porquê usar
ManimML stands out as an invaluable tool for anyone looking to create clear and engaging explanations of machine learning concepts. By leveraging the powerful Manim Community Library, it abstracts away much of the complexity of animation, allowing educators, researchers, and students to focus on the core ideas. Its modular design, with easily combinable primitive visualizations, makes it highly flexible for diverse educational content. Whether you're explaining basic neural networks or complex convolutional architectures, ManimML provides the visual clarity needed to enhance understanding.

## Links
*   **GitHub Repository:** [https://github.com/helblazer811/ManimML](https://github.com/helblazer811/ManimML){:target="_blank"}
*   **Academic Paper:** [https://arxiv.org/abs/2306.17108](https://arxiv.org/abs/2306.17108){:target="_blank"}