This repository contains IPython notebooks that implement a neural network from scratch without using ML libraries. It serves as a hands-on learning resource for understanding the underlying mechanics of neural networks, including forward propagation, backpropagation, and gradient descent. The code is accompanied by a blog post on the WildML blog that explains the concepts step by step.