Every neural-network tutorial I tried threw equations at me before I ever saw what was actually happening. I wanted the reverse: watch the activations flow forward, watch the loss bars shrink, watch backprop push gradients right-to-left across the layers.
So I built it. Here's a neural network that trains itself in front of you 👇
What you're actually seeing
Forward pass — particles flow left → right as activations propagate through the layers.
Loss — bars drop each epoch, and the output neurons glow red → gold → green as the error shrinks.
