Understanding Neural Networks, Visually (visualrambling.space)

Damar launched an interactive, beginner-focused visualization at visualrambling.space that walks users through how feedforward neural networks process data — a simple, hands-on explainer aimed at people overwhelmed by AI headlines who want to learn the basics. It’s a personal project (not an academic paper) that gestures toward pedagogical value for the AI/ML community: clear visual intuition can lower the barrier to entry, improve technical communication, and help newcomers grasp core mechanics before diving into training algorithms or math-heavy texts. Technically, the visualization demonstrates a classic MNIST-like example: an image’s pixel brightness values feed input neurons, each connection multiplies inputs by learned weights, receiving neurons sum these weighted inputs and apply an activation rule (a threshold in the demo) to decide whether they “fire.” Layers stack so early neurons detect simple features (lines/curves) and later layers build up higher-level patterns until output neurons indicate class predictions. The author deliberately stops short of showing how weights and thresholds are learned (training/backpropagation), inviting future expansion and community feedback. Overall, it’s a concise, approachable tool for building intuition about weighted sums, activations, and hierarchical feature extraction in neural nets.