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.
