Why Neural Networks Need He Init, Clipping, and Momentum

I built an interactive tutorial that shows why neural networks fail without proper initialization and optimization techniques. Here's what you'll find:

• Live code editor where you can watch networks train in real-time on x²
• Step-by-step progression from linear models → ReLU → deep networks
• Visual demonstrations of gradient explosion and dying ReLU problems
• Interactive examples showing how each fix (He init, clipping, momentum) solves specific issues
• Final section on vectorization and why frameworks use matrix operations

The approach is hands-on: start with a broken 2-layer network that hits NaN, then add fixes one at a time while watching the loss curve and predictions update. You can swap x² for cos(3x) or tweak hyperparameters to see how networks behave under different conditions.

Check it out at https://sbondaryev.dev/articles/he-init-clipping-momentum