Unveiling the Layers: Neural Networks from first principles

A Multilayer Perceptron from scratch using NumPy. Offers almost all basic functionalities . Suitable for classification and regression tasks. 一个用NumPy从零实现的多层感知机。提供几乎所有基本功能。适用于分类和回归任务。

A mechanistic interpretability framework visualizing the Universal Approximation Theorem. It deconstructs Neural Networks into weighted ReLU basis functions to reveal how models construct complex non-linear topologies from piecewise linear segments.

Custom Multilayer Perceptron implemented from scratch with NumPy for Wine Quality classification.

A NumPy-only Multilayer Perceptron (MLP) built from first principles to demystify neural networks, backpropagation, and optimization algorithms.

🔍 Explore how Multi-Layer Perceptrons work by visualizing function approximation through Neural Basis Decomposition and Mechanistic Interpretability techniques.