Deep Learning: Forward Pass & Backpropagation

Deep learning uses artificial neural networks stacked into multiple layers to analyze data and make predictions. This simulator makes that training loop visible in 3D: a small network of neurons and weighted connections sits in space, and every training cycle you watch a signal pulse travel forward through the layers to produce a prediction, followed by a gradient pulse traveling backward through the same connections to correct the weights. Because the math running underneath is a genuine feedforward network trained with real backpropagation and gradient descent — not a scripted animation — the loss curve, accuracy and edge weights you see actually evolve as the network learns to separate points inside a circle from points outside it. Adjust the learning rate, training speed and activation function to see how each changes how quickly and how stably the network converges.