This tutorial guides developers through building a neural network from scratch using NumPy to train on the MNIST dataset. It leverages a custom autograd engine called 'babygrad' to handle automatic differentiation, a core concept in modern deep learning frameworks like PyTorch and TensorFlow. The article details parsing MNIST data, defining a SimpleNN class with two weight matrices (W1, W2), implementing a softmax loss function, and executing a training loop.

By manually managing the computation graph and backpropagation, readers gain a fundamental understanding of how neural networks learn. The provided code demonstrates achieving over 96% accuracy, proving that complex deep learning concepts can be demystified through foundational implementation.