Backpropagation is the process of adjusting weights in a neural network by propagating errors backward from the output layer to improve accuracy. It represents the core learning mechanism in neural networks, enabling them to learn from mistakes and improve predictions.

NYD Application: Underlies the learning process in our AI tools that adapt to team coding styles and design preferences.

Example: "Through backpropagation, our model learned to better predict which Supabase queries would cause performance issues."