training stages

Visualizing the Weight Adjustments That Power Neural Network Training

Backpropagation doesn't have to feel like a black box. Think of it as a feedback loop: forward propagation makes a guess, then backpropagation measures the error and asks each weight, "How much did you contribute to that mistake?" Visualizing this as a gradient descending a landscape helps, weights adjust by rolling downhill toward lower error. Small, iterative nudges, not giant leaps. For a deeper dive into the geometry behind these calculations, our piece on exploring embeddings through cosine similarity offers a helpful parallel.