Neural Network Trainability Boundary is Fractal: Interactive Tool Reveals Tuning Reality
S_Conradi · x · 2026-07-24
A developer built an interactive notebook based on classic research, visually demonstrating that the boundary between learning rates that train and those that diverge is literally a fractal, much like the Mandelbrot set.
Key Elements
- Interactive Exploration: Supports drag-to-zoom, recomputing 65,536 full training runs on the GPU in 1 second at every depth to reveal infinite detail.
- Real Data Validation: The fractal property survives with real data (MNIST-1D), and the best learning rates live right up against this dangerous 'coastline'.
- AI-Assisted Dev: The author used Claude Code to write code, read live state, and edit cells directly within the running notebook to build this project.
Related event: Research Shows Neural Network Learning Rate Boundaries are Fractal(4 posts)→
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