This project is an educational resource and pedagogical framework designed to teach the fundamental mechanics of neural networks and gradient-based optimization. It provides a series of tutorials and code examples that guide users through building deep learning models from scratch, focusing on the implementation of core mathematical primitives and the underlying logic of backpropagation.
The project distinguishes itself by providing a custom automatic differentiation engine that tracks mathematical operations in a dynamic computational graph. By implementing reverse-mode automatic differentiation and topological sort execution, it allows users to compute gradients for complex expressions without manual derivation, providing a transparent view into how neural network architectures are structured and trained.
The repository covers the foundational aspects of machine learning, including the construction of layers and activation functions using scalar-based primitive operations. These tools enable the manual assembly of neural networks, facilitating a conceptual understanding of how systems learn patterns and perform predictions. The content is delivered through a series of Jupyter Notebooks that serve as a structured course on deep learning mechanics.
