Jun 27
The Vanishing Gradient Problem
For decades, artificial neural networks promised something remarkable: stack enough layers together, and a machine could learn almost anything. The logic seemed sound. More layers meant more abstraction, more complexity, more power. If one layer could detect edges in an image, maybe ten layers could detect faces. Maybe a hundred layers could understand the world. There was just one problem. The…