The loss landscape is the surface formed by plotting the loss function over all possible parameter values — its shape determines how easy or hard optimization is. A smooth, convex landscape has a single clear minimum, while the high-dimensional landscapes of deep networks contain many local minima, saddle points, and flat regions. Research into loss landscape geometry helps explain why overparameterized networks generalize well and why certain architectures or learning rate schedules succeed. Techniques like learning rate warmup and sharpness-aware minimization explicitly navigate landscape topology.