Neural network loss surface visualization with gradient descent trajectories
Click canvas to place optimizer start point
The loss landscape of a neural network is a high-dimensional surface whose geometry determines training dynamics. Sharp minima generalize poorly (Hochreiter & Schmidhuber 1997); flat minima correlate with better generalization. Adaptive optimizers (Adam) navigate ravines and saddle points faster than vanilla SGD by tracking per-parameter gradient statistics.