Getting Started with Neural Networks: A Practical Guide

Prerequisites

This guide assumes basic Python knowledge and high-school level math. We'll build a neural network from scratch — no TensorFlow, no PyTorch, just NumPy and pure understanding.

What is a Neural Network?

At its core, a neural network is a function approximator. Given inputs, it learns to produce desired outputs by adjusting internal parameters (weights and biases) through a process called training.

The Perceptron

The simplest neural network is a single perceptron — one "neuron" that takes weighted inputs, sums them, and passes the result through an activation function.

Building Forward Propagation

Forward propagation is how a neural network makes predictions. Data flows from input layer → hidden layers → output layer, with each layer applying weights, biases, and activation functions.

Backpropagation: Learning from Mistakes

The magic of neural networks lies in backpropagation — an elegant algorithm that calculates how much each weight contributed to the error, then adjusts it accordingly. It's essentially the chain rule from calculus applied recursively through the network.

Putting It Together

Our complete neural network classifies handwritten digits from the MNIST dataset with 97% accuracy — all in about 100 lines of NumPy code. The full code is on GitHub.

Next Steps

From here, you can explore convolutional networks for images, recurrent networks for sequences, and transformers for language. But the fundamentals you learned today apply to all of them.