Neural Networks Explained: From Pixels to Predictions

How does a neural network look at a handwritten image and decide that it’s a 5? In this video, we go inside a neural network and follow the complete journey of an image — from raw pixels to a final prediction. We start with a simple question: why can't we just write rules for recognizing handwriting? Then we build the network from the ground up: 784 pixels → 12 neurons → 12 neurons → 12 neurons → 10 outputs Along the way, we explore: Why rule-based systems struggle with handwritten digits How an image becomes 784 numerical values What input, hidden, and output layers actually do What neurons, weights, and biases mean How a neuron calculates its weighted sum Why activation functions are necessary Sigmoid vs ReLU Why ReLU is preferred in deep networks How matrix multiplication makes neural networks practical Why a newly initialized network is essentially making random guesses What loss means How gradients tell the network what needs to change How backpropagation begins to fix those mistakes The goal isn't to treat a neural network as a black box. It's to understand what is happening inside the box. Pixels in. Numbers transform. Prediction out. And then comes the interesting part: how does the network know exactly which weights to change? That's where backpropagation takes over.