Developer builds neural network from scratch in Python using only NumPy

A developer created a minimal neural network entirely from scratch using Python and NumPy, deliberately avoiding frameworks like PyTorch to deepen understanding of how training actually works. The network implements a three-layer architecture with ReLU and sigmoid activations, binary cross-entropy loss, and gradient descent via backpropagation. A synthetic 200-sample binary classification dataset was used to demonstrate the full training loop across multiple epochs. The project walks through each core concept — forward propagation, loss calculation, and weight updates — step by step without relying on any automatic differentiation tools. The author argues that manually building a network clarifies what high-level framework calls like loss.backward() and optimizer.step() are actually doing under the hood.