# vdumoulin/conv_arithmetic

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## Links

- GitHub: https://github.com/vdumoulin/conv_arithmetic
- awesome-repositories: https://awesome-repositories.com/repository/vdumoulin-conv-arithmetic.md

## Description

This project provides a collection of visual guides, technical documentation, and animation generation tools designed to explain the mathematical mechanics of neural network layer operations. It serves as an educational resource for understanding the architecture and data mapping processes involved in deep learning.

The toolset distinguishes itself by programmatically generating visual representations of standard, transposed, and dilated convolution layers. By utilizing a declarative configuration model, it maps mathematical parameters—such as kernel sizes, strides, and padding—to coordinate-based grid renderings. This pipeline produces both static diagrams and procedural animation sequences that illustrate how input data maps to output features.

Beyond its visualization capabilities, the project includes utilities for generating structured technical reports that detail the mathematical foundations of convolution layers. These assets are intended to support academic study and research into feature extraction processes within computer vision.

## Tags

### Artificial Intelligence & ML

- [Convolutional Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-neural-networks.md) — Offers a comprehensive toolset for visualizing the mathematical mechanics of standard, transposed, and dilated convolution layers.
- [Convolutional Operations](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-operations.md) — Visualizes the mathematical mechanics of convolution operations using animated sequences and diagrams. ([source](https://github.com/vdumoulin/conv_arithmetic#readme))
- [Spatial Mapping Calculators](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-operations/input-padding-utilities/output-padding-controllers/spatial-dimension-controllers/spatial-mapping-calculators.md) — Calculates output dimensions and spatial relationships for convolution layers based on kernel, stride, and padding parameters.
- [Computer Vision Curricula](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-curricula.md) — Provides research-oriented educational materials for understanding feature extraction in computer vision.
- [Visualization Generators](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-operations/visualization-generators.md) — Produces animated and static visual assets representing various convolution configurations for educational purposes. ([source](https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md))

### Education & Learning Resources

- [Deep Learning Education](https://awesome-repositories.com/f/education-learning-resources/deep-learning-education.md) — Provides educational resources explaining the mathematical mechanics of deep learning layers through visual aids.

### Graphics & Multimedia

- [Mathematical Animation Engines](https://awesome-repositories.com/f/graphics-multimedia/media-production-suites/animation-tools/mathematical-visualization-engines/mathematical-animation-engines.md) — Generates precise, programmatic animations of geometric transformations for convolution operations.
- [Coordinate Systems](https://awesome-repositories.com/f/graphics-multimedia/visualization-mapping/visualization-frameworks/coordinate-systems.md) — Provides utilities for rendering mathematical coordinate grids to visualize neural network layer operations.

### Scientific & Mathematical Computing

- [Mathematical Animation](https://awesome-repositories.com/f/scientific-mathematical-computing/data-modeling-processing/mathematical-animation.md) — Generates animated sequences that illustrate complex mathematical operations within deep learning frameworks.

### Content Management & Publishing

- [Documentation Generators](https://awesome-repositories.com/f/content-management-publishing/content-management-systems/content-architecture-modeling/documentation-tooling/generation-publishing/documentation-generators.md) — Generates technical reports and documentation by processing source configurations into structured mathematical explanations. ([source](https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md))

### Software Engineering & Architecture

- [Declarative Configurations](https://awesome-repositories.com/f/software-engineering-architecture/declarative-configurations.md) — Uses structured configuration schemas to define and drive the generation of convolution layer visualizations.

### Development Tools & Productivity

- [Educational Asset Pipelines](https://awesome-repositories.com/f/development-tools-productivity/build-tooling/asset-file-management/asset-processing-pipelines/asset-transformation-pipelines/static-asset-pipelines/educational-asset-pipelines.md) — Automates the conversion of mathematical convolution parameters into structured image and animation files for documentation.